• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.128-135
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• 2021

Thin-walled tubes have been widely used as energy absorbing devices because they are light and have high energy-absorption efficiency. However, the downside is that conventional thin-walled tubes usually exhibit an excessive initial peak crushing force (IPCF) and a large fluctuation in the load-displacement curve, and thus lack stability as energy absorbing devices. Corrugated tubes were introduced to reduce IPCF and to increase the stability of collision energy-absorbing devices. Since the performance of corrugated tubes is highly influence by geometry, design optimization methods can be utilized to optimize the performance of corrugated tubes. In this paper, we utilize shape design optimization based on an adaptive surrogate model for crashworthiness analysis. The amplitude and wavelength of the corrugation, as well as curvature changes in the features, are the design variables. A morphing methodology is adopted to perform shape design parameterization. Through numerical examples, we compare optimal design results based on the adaptive surrogate model, with optimal results based on conventional surrogate models, and we show that direct optimal design methods produce more efficient results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.6
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• pp.537-546
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• 2010

This research work contributed to a study for the procedure and methodology to assess the fatigue durability for a composite torque link of helicopter landing gear, which was newly developed and fabricated by the resin transfer moulding technique to interchange with metal component. The simulated load spectrum anticipated to be applied to the torque link during its operation life was generated using an advanced method of probabilistic random process, and the fatigue durability was evaluated by the strength degradation approach on the basis of material test data. The full scale fatigue test was also performed and compared with the analysis results.

• Journal of KIISE:Information Networking
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• v.35 no.4
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• pp.292-300
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• 2008

In multi-hop wireless networks like Wireless Mesh Networks (WMN) and Wireless Sensor Networks (WSN), nodes often rely on batteries as their power source. In such cases, energy efficient routing is critical. Many schemes have been proposed to find the most energy efficient path, but most of them do not achieve optimality on network lifetime. Once found, the energy efficient path is constantly used such that the energy of the nodes on the path is depleted quickly. As an alternative, the approaches that dynamically change the path at run time have also been proposed. These approaches, however, involve high overhead of establishing multiple paths. In this paper, we first find an optimal multi-path routing using LP. Then we apply an approximation algorithm to derive a near-optimal solution for single-path routing. We compare the performance of the proposed scheme with several other existing algorithms through simulation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.3
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• pp.228-235
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• 2010

This paper deals with an autonomous flight controller design problem for a tilt-rotor aircraft in rotary-wing mode. The inner-loop algorithm is designed using the output-based approximate feedback linearization. The model error originated from the feedback linearization is cancelled within allowable tolerance by using single-hidden-layer neural network. According to Lyapunov direct stability theory, the adaptive update law is derived to run the neural network on-line, which is based on the linear observer dynamics. Moreover, the outer-loop algorithm is designed to track the trajectory generated from way-point guidance. Especially, heading and flight-path angle line-of-sight guidance are applied to the outer-loop to improve accuracy of the landing tracking performance. The 6-DOF nonlinear simulation shows that the overall performance of the flight control algorithm is satisfactory even though the collective input response shows instantaneous actuator saturation for a short time due to the lack of the neural network and the saturation protection logic in that loop.

• Journal of the Society of Naval Architects of Korea
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• v.33 no.1
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• pp.98-108
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• 1996

In the reliability analysis of general structures, the limit state equations are implicit and cannot be described in closed form. Thus, sampling methods such as the Crude Monte-Carlo simulation, and probabilistic FEM are often used, but these methods are not so effective in view of computational cost, because a number of structural analysis are required and the derivatives must be calculated for probabilistic FEM. Alternatively the response surface approach, which approximates the limit state surface by using several results of structural analysis in the region adjacent to MPFP, could be applied effectively. In this paper, the central composite design, Bucher-Bourgund method and the approximation method using artificial neural network are studied for the calculation of probability of failure by the response surface method. Through the example comparisons, it is found that Bucher-Bourgund method is very effective and Neural network method for the reliability analysis is comparable with other methods. Specially, the central composite design method is found to be rational and useful in terms of mathematical consistency and accuracy.

• Geophysics and Geophysical Exploration
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• v.14 no.3
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• pp.191-202
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• 2011

Reserve-time migration (RTM) using a two-way wave equation is one of the most accurate migration techniques. RTM has been conducted by assuming that subsurface media are isotropic. However, anisotropic media are commonly encountered in reality. Conventional isotropic RTM may yield inaccurate results for anisotropic media. In this paper, we develop RTM algorithms for vertical transversely isotropic media (VTI) and tilted transversely isotropic media (TTI). For this, the pseudo-acoustic wave equations are used. The modeling algorithms are based on the high-order finite-difference method (FDM). The RTM algorithms are composed using the cross-correlation imaging condition or the imaging condition using virtual sources. By applying the developed RTM algorithms to the Hess VTI and BP TTI models, we could obtain better images than those obtained by the conventional isotropic RTM.

• Journal of Korea Game Society
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• v.18 no.5
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• pp.123-132
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• 2018

Various methods have been proposed to efficiently animate the motion of soft objects in realtime. In order to maintain the topology between the elements of the objects, it is required to employ constraint forces, which limit the size of the time steps for the numerical integration and reduce the efficiency. To tackle this, an implicit method with larger steps was proposed. However, the method is, in essence, a linear system with a large matrix, of which solution requires heavy computations. Several approximate methods have been proposed, but the approximation is obtained with an increased damping and the loss of accuracy. In this paper, new integration method based on harmonic oscillation with better stability was proposed, and it was further stabilized with the hybridization with approximate implicit method. GPU parallelism can be easily implemented for the method, and large-scale soft objects can be simulated in realtime.

• Journal of Advanced Navigation Technology
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• v.7 no.1
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• pp.14-21
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• 2003

We investigated the optimum pump light power compensating distorted WDM signal due to both chromatic dispersion and self phase modulation (SPM). The considered system is $3{\times}40$ Gbps intensity modulation direct detection (IM/DD) WDM transmission system with path-averaged intensity approximation (PAIA) mid-span spectral inversion (MSSI) as compensation method. This system have highly nonlinear dispersion shifted fiber (HNL-DSF) as nonlinear medium of optical phase conjugator (OPC) in the mid-way of total transmission line. We confirmed that HNL-DSF is an useful nonlinear medium in OPC for wideband WDM transmission, and the excellent compensation is obtained when the pump light power of HNL-DSF OPC was selected to equalize the conjugated light power into the second half fiber section with the input WDM signal light power depending on total transmission length. By this approach, it is verified the possibility to realize a long-haul high capacities WDM system by using PAIA MSSI compensation method, which have HNL-DSF OPC with optimal pump light power depending on transmission length.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.77-84
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• 2022

In this paper, we propose a novel unsupervised feature selection method. Conventional unsupervised feature selection method defines virtual label and uses a regression analysis that projects the given data to this label. However, since virtual labels are generated from data, they can be formed similarly in the space. Thus, in the conventional method, the features can be selected in only restricted space. To solve this problem, in this paper, features are selected using orthogonal projections and low-rank approximations. To solve this problem, in this paper, a virtual label is projected to orthogonal space and the given data set is also projected to this space. Through this process, effective features can be selected. In addition, projection matrix is restricted low-rank to allow more effective features to be selected in low-dimensional space. To achieve these objectives, a cost function is designed and an efficient optimization method is proposed. Experimental results for six data sets demonstrate that the proposed method outperforms existing conventional unsupervised feature selection methods in most cases.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1081-1090
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• 2022

The face recognition model is used for identity recognition of smartphones, providing convenience to many users. As a result, the security review of the DNN model is becoming important, with adversarial attacks present as a well-known vulnerability of the DNN model. Adversarial attacks have evolved to decision-based attack techniques that use only the recognition results of deep learning models to perform attacks. However, existing decision-based attack technique[14] have a problem that requires a large number of queries when generating adversarial examples. In particular, it takes a large number of queries to approximate the gradient. Therefore, in this paper, we propose a method of generating adversarial examples using orthogonal space sampling and dimensionality reduction sampling to avoid wasting queries that are consumed to approximate the gradient of existing decision-based attack technique[14]. Experiments show that our method can reduce the perturbation size of adversarial examples by about 2.4 compared to existing attack technique[14] and increase the attack success rate by 14% compared to existing attack technique[14]. Experimental results demonstrate that the adversarial example generation method proposed in this paper has superior attack performance.