• 한국항만학회지
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• 제13권2호
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• pp.389-398
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• 1999

In the biological immunity, the immune system of organisms regulates the antibody and T-cells to protect the attack from the foreign materials which are virus, germ cell, and other antigens, and supports their stable state. It has similar characteristics that has the adaptation and robustness to overcome disturbances and to control the plant of engineering application. In this paper, we build a model of the T-cell regulated immune response mechanism. We have also designed an immune response controller(IRC) focusing on the T-cell regulated immune response of the biological immune system that include both a help part to control the response and a suppress part to adjust system stabilization effect. We show some computer simulation to control the vibration of building structure system with strong wind forces excitation also demonstrate the efficiency of the proposed controller for applying a practical system even with existing nonlinear terms.

• 전기학회논문지P
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• 제54권2호
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• pp.73-79
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• 2005

This paper is proposed hybrid artificial intelligent (HAI) controller based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed estimation of induction motor using a closed-loop state observer. The rotor position is calculated through the stator flux position and an estimated flux value of rotation reference frame. A closed-loop state observer is implemented to compute the speed feedback signal. The results of analysis prove that the proposed control system has strong robustness to rotor parameter variation, and has good steady-state accuracy and transitory response.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권4호
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• pp.177-183
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• 2001

In this paper, a new variable structure controller is designed for the tracker control of uncertain general plants so that the output of plants can controlled to a given arbitrary point in state space. By using the error between the steady state value of the output and the given reference, the sliding surface is defined, in advance, the surface from an initial state to the given reference without any reaching phase. A corresponding control input to satisfy the existence condition of the sliding mode is suggested to control the output on the predefined surface. Therefore the output controlled by the proposed controller is completely robust and identical to that of the sliding surface. Through an example, the usefulness is verified.

• Steel and Composite Structures
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• 제21권6호
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• pp.1389-1410
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• 2016

This paper proposes a hybrid of topological derivative-based level set method (LSM) and isogeometric analysis (IGA) for structural topology optimization. In topology optimization a significant drawback of the conventional LSM is that it cannot create new holes in the design domain. In this study, the topological derivative approach is used to create new holes in appropriate places of the design domain, and alleviate the strong dependency of the optimal topology on the initial design. Furthermore, the values of the gradient vector in Hamilton-Jacobi equation in the conventional LSM are replaced with a Delta function. In the topology optimization procedure IGA based on Non-Uniform Rational B-Spline (NURBS) functions is utilized to overcome the drawbacks in the conventional finite element method (FEM) based topology optimization approaches. Several numerical examples are provided to confirm the computational efficiency and robustness of the proposed method in comparison with derivative-based LSM and FEM.

• 반도체디스플레이기술학회지
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• 제17권4호
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• pp.105-108
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• 2018

Face Detection is an essential first step of the face recognition, and this is significant effects on face feature extraction and the effects of face recognition. Face detection has extensive research value and significance. In this paper, we present and analysis the principle, merits and demerits of the classic AdaBoost face detection and ASM algorithm based on point distribution model, which ASM solves the problems of face detection based on AdaBoost. First, the implemented scheme uses AdaBoost algorithm to detect original face from input images or video stream. Then, it uses ASM algorithm converges, which fit face region detected by AdaBoost to detect faces more accurately. Finally, it cuts out the specified size of the facial region on the basis of the positioning coordinates of eyes. The experimental result shows that the method can detect face rapidly and precisely, with a strong robustness.

• Nuclear Engineering and Technology
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• 제55권1호
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• pp.285-294
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• 2023

Autonomous radiation source detection has long been studied for radiation emergencies. Compared to conventional data-driven or path planning methods, deep reinforcement learning shows a strong capacity in source detection while still lacking the generalized ability to the geometry in unknown environments. In this work, the detection task is decomposed into two subtasks: exploration and localization. A hierarchical control policy (HC) is proposed to perform the subtasks at different stages. The low-level controller learns how to execute the individual subtasks by deep reinforcement learning, and the high-level controller determines which subtasks should be executed at the current stage. In experimental tests under different geometrical conditions, HC achieves the best performance among the autonomous decision policies. The robustness and generalized ability of the hierarchy have been demonstrated.

• Steel and Composite Structures
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• 제52권6호
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• pp.695-711
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• 2024

Suspension bridges are critical to urban transportation, but those in earthquake-prone areas face unique challenges. In the event of a moderate or strong earthquake, conventional linear theory-based approaches for detecting bridge damage become inadequate. This study presents an efficient method for identifying damage in suspension bridges using time history nonlinear inelastic analysis. A practical advanced analysis program is employed to model cable-supported bridges with low computational cost, generating a dataset for four hybrid models: PSO-DT, PSO-RF, PSO-XGB, and PSO-CGB. These models combine decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with particle swarm optimization (PSO) to capture nonlinear correlations between displacement response and damage. Principal component analysis reduces dataset dimensions, and PSO selects the optimal model. A numerical case study of a suspension bridge under simulated earthquake conditions identifies PSO-XGB as the best model for predicting stiffness reduction. The results demonstrate the method's robustness for nonlinear damage detection in suspension bridges under earthquake excitation.

• Smart Structures and Systems
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• 제15권2호
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• pp.447-468
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• 2015

Modal identification of civil engineering structures based on ambient vibration measurement has been widely investigated in the past decades, and a variety of output-only operational modal identification methods have been proposed. However, vibration modes, even fundamental low-order modes, are not always identifiable for large-scale structures under ambient vibration excitation. The identifiability of vibration modes, deficiency in modal identification, and criteria to evaluate robustness of the identified modes when applying output-only modal identification techniques to ambient vibration responses were scarcely studied. In this study, the mode identifiability of the cable-stayed Ting Kau Bridge using ambient vibration measurements and the influence of the excitation intensity on the deficiency and robustness in modal identification are investigated with long-term monitoring data of acceleration responses acquired from the bridge under different excitation conditions. It is observed that a few low-order modes, including the second global mode, are not identifiable by common output-only modal identification algorithms under normal ambient excitations due to traffic and monsoon. The deficient modes can be activated and identified only when the excitation intensity attains a certain level (e.g., during strong typhoons). The reason why a few low-order modes fail to be reliably identified under weak ambient vibration excitations and the relation between the mode identifiability and the excitation intensity are addressed through comparing the frequency-domain responses under normal ambient vibration excitations and under typhoon excitations and analyzing the wind speeds corresponding to different response data samples used in modal identification. The threshold value of wind speed (generalized excitation intensity) that makes the deficient modes identifiable is determined.

• 응용통계연구
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• 제27권3호
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• pp.397-406
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• 2014

최소밀도함수승간격 추정법은 Baus 등 (1998)에 의해 처음 소개된 이후 많은 관심의 대상이 되었다. 최소밀도함수승간격 추정량은 우수한 로버스트 성질을 갖고 효율성도 최우추정량에 필적한 것으로 알려져 있다. 본 논문에서는 생물정보학에서 사용되는 노말-지수 분포에 근거한 추정량을 최소밀도함수승간격 추정법을 사용하여 구하는 방법을 다루고자 한다. 그런데 그 과정에서 간격을 적분을 통해 구하는 것이 매우 어려움으로 인해 직접적인 적분 대신 라플라스 근사를 시도할 것을 제안한다. 그 결과 추정량이 다소 효율성이 줄어들지만 로버스트 성질을 갖고 있음을 수학적 방법과 모의실험을 통하여 보였다.

• 대한전자공학회논문지SP
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• 제40권6호
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• pp.41-50
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• 2003

디지털 워터마킹 기술은 디지털 콘텐츠의 불법 복제를 방지하기 위해 디지털 데이터에 사람이 감지할 수 없는 정보를 은닉한다. 본 논문에서는 3차원 메쉬 모델(mesh model)에 대한 적응형 워터마킹 방법을 제안한다 본 논문에서 제안한 방법에서는 서로 이웃하는 꼭지점 좌표들 사이의 공간적 상관성에 따라 워터마크를 삽입하며, 이는 사람의 눈에 잘 감지되지 않는 지역에 워터마크를 강하게 삽입하고 그렇지 않은 지역에는 약하게 삽입하는 적응형 워터마킹 기술이다., 우선, 3차원 메쉬 모델을 운행(traversing)하여 삼각형 스트립(triangle strip)을 생성하고, 모든 꼭지점 좌표를 구 좌표계(spherical coordinate system)로 변환시킨다. 그리고, 3차원 모델의 지역적 외관을 결정하는 꼭지점 좌표 값들의 변화량을 계산한 후, 워터마크 신호를 계산한 변화량의 크기에 따라 유연하게 꼭지점 좌표 값에 삽입시킨다. 본 논문에서 제안한 워터마크 방법이 워터마크 신호의 비지각성(imperceptibility)을 크게 개선시킬 수 있음을 실험을 통해 검증했으며, 제안한 방법의 강인성 (robustness)에 대한 BER (bit error rate) 결과를 제시하였다.