• 한국지진공학회논문집
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• 제27권6호
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• pp.303-310
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• 2023

The equivalent static load for non-structural elements has a limitation in that the sloshing effect and the interaction between the fluid and the water tank cannot be considered. In this study, the equations to evaluate the impulse and convective components in the design codes and previous research were compared with the shaking table test results of a rectangular water tank with flexible wall panels. The conclusions of this study can be summarized as follows: (1) It was observed that the natural periods of the impulsive component according to ACI 350.3 were longer than system identification results. Thus, ACI 350.3 may underestimate the earthquake load in the case of water tanks with flexible walls. (2) In the case of water tanks with flexible walls, the side walls deform due to bending of the front and back walls. When such three-dimensional fluid-structure interaction was included, the natural period of the impulsive component became similar to the experimental results. (3) When a detailed finite element (FE) model of the water tank was unavailable, the assumption S_ai = S_DS could be used, resulting in a reasonably conservative design earthquake load.

• Journal of Electrical Engineering and Technology
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• 제7권3호
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• pp.304-311
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• 2012

So far, the importance for an accurate load model has been constantly raised and its necessity would be further more emphasized. Currently used load model for analysis of power system in Korea was developed 10 years ago, which is aggregated by applying the statistically estimated load compositions to load models based on individual appliances. As modern appliances have diversified and rapidly changed, the existing load model is no longer compatible with current loads in the Korean power system. Therefore, a measurement based load model is more suitable for modern power system analysis because it can accurately include the load characteristics by directly measuring target load. This paper proposes a ZIP model employing a Kalman-filter as the estimation algorithm for the model parameters. The Kamlan-filter based parameter identification offers an advantage of fast parameter determination by removing iterative calculation. To verify the proposed load model, the four-second-interval real data from the Korea Energy Management System (K-EMS) is used.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(4)
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• pp.251-254
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• 2000

This paper proposes an enhanced algorithm for person identification system utilizing hand vein pattern. The conventional algorithm does not cope with distortion caused by image rotation caused by misplaced hands on the imaging device. A straightforward approach to consider the rotaional compensation required too much computational load, thus, we devised an approach to expect the rotation direction along with image translation, reducing the compuational requirement dramatically In this paper, we present the details of the algorithm with experimental results with the new algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권4호
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• pp.327-332
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• 2009

This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.

• 한국안전학회지
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• 제20권2호
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• pp.119-126
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• 2005

Load bearing structural members in a wide variety of applications accumulate damage over their service life. From a standpoint of both safety and performance, it is desirable to monitor the occurrence, location, and extent of such damage. Structures require complicated element models with a number of degrees of freedom in structural analysis. During experiment much effort and cost is needed for measuring structural parameters. The sparseness and errors of measured data have to be considered during the parameter estimation Of Structures. In this paper we introduces damage identification algorithm by a system identification(S.I) using static and dynamic response. To study the behaviour of the estimators in noisy environment Using Monte Carlo simulation and a data measured perturbation scheme is adopted to investigate the influence of measurement errors on identification results. The assessment result by static and dynamic response were compared, and the efficiency and applicabilities of the proposed algorithm are demonstrated through simulated static and dynamic responses of a truss bridge. The assessment results by each method were compared and we could observe that the 5.1 method is superior to the other conventional methods.

• Computers and Concrete
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• 제20권5호
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• pp.617-625
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• 2017

Prestress force identification (PFI) is crucial to maintain the safety of prestressed concrete bridges. A synergic identification method has been proposed recently by the authors that can determine the prestress force (PF) and the excitation force simultaneously in prestressed concrete beams with good accuracy. In this paper, the ability of this method in the application with prestressed concrete box-girder bridges is demonstrated. A reasonable assumption is made to capture the similarity of the dynamic behavior of the prestressed concrete box-girder bridge and a beam under a certain loading scenario, and the feasibility of this method for application in a prestressed box-girder bridge is affirmed. A comprehensive laboratory test program is conducted, and the effects of PF, excitation, measuring time and uncertainties are studied. Results show that the proposed method can predict the PF and the excitation force in a prestressed concrete box-girder accurately and has a great robustness against uncertainties.

• 한국지진공학회논문집
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• 제9권1호통권41호
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• pp.25-32
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• 2005

지진하중을 받는 구조물은 모드참여계수에 의하여 각각의 모드에 지진하중이 분배, 전달된다. 이러한 특성 때문에 모드참여계수는 지진하중을 받는 구조물의 해석에서 매우 중요한 요소이다. 그러나 이상화된 해석 구조물의 모드참여계수는 해석적 모델링이나 시공오차 등에 의하여 실 구조물의 참여계수와 다르기 때문에 실제 거동을 예측, 반영하기에 한계가 있다. 본 연구에서는 시스템 식별기술과 $H^{\infty}$ 최적 모델 응축법을 활용하여, 구조물의 1차 모드참여계수를 산정하는 기법을 제안한다. 이 기법은 시스템 식별로부터 구현된 상태방정식을 전형의 상태방정식과 비교하는 과정에서 시스템의 가제어, 가관측 행렬의 비에 의하여 결정된다. 본 연구에서 제안한 모드참여계수산정기법은 단자유도, 다자유도 전단구조물에 대한 수치해석을 통하여 검증하였다.

• 한국공간정보시스템학회 논문지
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• 제10권2호
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• pp.13-24
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• 2008

차세대 실감 내비게이션 시스템은 2D 기반 내비게이션 시스템의 단점을 보완하고 보다 안전한 운전을 할 수 있도록 다양한 서비스를 제공하기 위해 연구되고 있다. 실감 내비게이션 시스템 차선인식과 도로시설물 객체 DB, 교차로 인식 모듈등의 기능 블록들로 구성된다. 본 논문에서는 실감내비게이션의 중요 요소 중 하나인 교차로 인식을 위한 신호등과 표지판 인식 시스템을 개발하였다. 개발된 알고리듬은 색상 정보를 이용해 인식 대상을 검출하고 객체의 특징을 이용하여 신호등과 표지판을 객체별로 인식할 수 있도록 하였으며 실험을 통해 검증하였다. 실험결과 신호등의 경우 60-30m의 거리에서 평균90%의 인식률을 보였으며, 표지판의 경우 90-40m의 거리에서 평균 97%의 인식률을 보였고, 프레임 당 평균 처리시간이 46msec로서 실시간 처리가 가능함을 보였다.

• 정보과학회 논문지
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• 제44권10호
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• pp.1071-1080
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• 2017

오늘날 복잡한 네트워크 망을 가지게 됨에 따라 네트워크 기기들의 자산식별은 관리 및 보안관점에서 중요한 사항으로 대두되고 있다. 이러한 자산들은 네트워크 망에 연결되어 있기 때문에 네트워크망 구조를 알아내고, 각 자산의 위치 및 연결 상태를 확인하는 것 또한 중요하다. 이는 네트워크 구조상의 취약점들을 밝혀내는데 사용되어지고, 이를 통하여 취약점을 보완할 수 있다. 하지만 적은 리소스를 가지는 사물인터넷의 네트워크 망에서는 네트워크 구조를 알아내기 위하여 모니터들이 보내는 Traceroute 패킷이 사물인터넷 기기들에게 과부하를 줄 수 있다. 이를 위하여 본 논문에서는 기존에 사용 되던 더블 트리 알고리즘을 효과적으로 발전시킴으로써 사물인터넷이 이루는 네트워크 망의 부하를 줄인다. 이러한 부하 균형을 이루기 위하여 이 논문에서는 새로운 목적지 매칭 알고리즘을 제시하고, 통계학적으로 현재 탐색하고 있는 경로와 가장 겹치지 않은 경로로 탐색을 시도한다. 이를 통해서 네트워크의 부하 균형을 이루고, 부가적으로 모니터의 리소스 사용을 균등하게 한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.588-591
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• 2003

This paper presents a new short-term load forecasting system using data mining. Since the electric load has very different pattern according to the day, it definitely gives rise to the forecasting error if only one forecasting model is used. Thus, to resolve this problem, the fuzzy model-based classifier and predictor are proposed for the forecasting of the hourly electric load. The proposed classifier is the multi-input and multi-output fuzzy system of which the consequent part is composed of the Bayesian classifier. The proposed classifier attempts to categorize the input electric load into Monday, Tuesday$\sim$Friday, Saturday, and Sunday electric load, Then, we construct the Takagi-Sugeno (T-S) fuzzy model-based predictor for each class. The parameter identification problem is converted into the generalized eigenvalue problem (GEVP) by formulating the linear matrix inequalities (LMIs). Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.