• 한국지반공학회지:지반
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• 제1권1호
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• pp.47-58
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• 1985

본 연구에서는 토성정수와 하중을 종래의 단일치 대신 확률변수로 취급하여 얕은 기초의 신뢰도 해석을 시도하였다. 즉 토성정수와 하동을 점추정하는 대신 구간추정하여 얕은 기초의 안정성을 종래의 안전률 대신 파괴확률로 표시할 수 있었다. 이른바 허용안전률이 별다른 이론적 배경이 없는 경험의 소산에 불과하며 안전률이 수치적 안전척경가 되지 못한다는 태책을 감안하면 파괴확율은 보단 합리적인 신뢰도의 표현수단이 될 수 있다고 생각한다. 지전의 지지력과 하중은 정규분포, 대수총규분포 및 베타분포하는 것으로 가정하였고 이들 각 분포에 따르는 다수의 확률변수를 생성하여 오차전파방법으로 파괴확률을 산정하는 전산 프로그램을 개발하였다. 이 전산 프로그램을 이용하여 Case study를 하고 그 결과를 분석하였다.

• 대한임베디드공학회논문지
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• 제4권4호
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• pp.195-200
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• 2009

As 4G mobile communication systems require high transmission rates with reliability, the need for efficient error correcting code is increasing. In this paper, a novel LDPC (Low Density Parity Check) decoder is introduced. The LDPC code is one of the most popular error correcting codes. In order to improve performance of the LDPC decoder, we use SNR (Signal-to-Noise Ratio) estimation results to adjust coefficients of modified UMP-BP (Uniformly Most Probable Belief Propagation) algorithm which is one of widely-used LDPC decoding algorithms. An advantage of Modified UMP-BP is that it is amenable to implement in hardware. We generate the optimal values by simulation for various SNRs and coefficients, and the values are stored in a look-up table. The proposed decoder decides coefficients of the modified UMP-BP based on SNR information. The simulation results show that the BER (Bit Error Rate) performance of the proposed LDPC decoder is better than an LDPC decoder using a conventional modified UMP-BP.

• 한국추진공학회지
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• 제15권2호
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• pp.1-7
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• 2011

다양한 비행환경에서 장시간 체공하며 운용되는 UAV에서 추진시스템을 신뢰성 있게 운용하는 것은 매우 중요하다. 이런 UAV에 사용되는 터보프롭 엔진의 정확한 손상진단은 신뢰성과 이용률을 향상시킬 수 있다. 본 연구에서는 엔진 측정 파라미터들의 변화로부터 퍼지 이론을 적용하여 손상된 구성품을 식별한 후 훈련된 신경망 알고리즘을 식별된 손상 패턴에 적용하여 손상된 양을 정확히 진단할 수있는 방법을 제안하였다. 이렇게 제안된 진단 방법은 단일손상과 다중손상 모두 진단할 수 있다.

• Computers and Concrete
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• 제21권1호
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• pp.47-54
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• 2018

Artificial neural network models can be successfully used to simulate the complex behavior of many problems in civil engineering. As compared to conventional computational methods, this popular modeling technique is powerful when the relationship between system parameters is intrinsically nonlinear, or cannot be explicitly identified, as in the case of concrete behavior. In this investigation, an artificial neural network model was developed to assess the residual compressive strength of self-compacted concrete at elevated temperatures ($20-900^{\circ}C$) and various relative humidity conditions (28-99%). A total of 332 experimental datasets, collected from available literature, were used for model calibration and verification. Data used in model development incorporated concrete ingredients, filler and fiber types, and environmental conditions. Based on the feed-forward back propagation algorithm, systematic analyses were performed to improve the accuracy of prediction and determine the most appropriate network topology. Training, testing, and validation results indicated that residual compressive strength of self-compacted concrete, exposed to high temperatures and relative humidity levels, could be estimated precisely with the suggested model. As illustrated by statistical indices, the reliability between experimental and predicted results was excellent. With new ingredients and different environmental conditions, the proposed model is an efficient approach to estimate the residual compressive strength of self-compacted concrete as a substitute for sophisticated laboratory procedures.

• JSTS:Journal of Semiconductor Technology and Science
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• 제13권6호
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• pp.622-634
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• 2013

The circuit level implementation of nanoscale Insulated Shallow Extension Silicon On Nothing (ISE-SON) MOSFET has been investigated and compared with the other conventional devices i.e. Insulated Shallow Extension (ISE) and Silicon On Nothing (SON) using the ATLAS 3D device simulator. It can be observed that ISE-SON based inverter shows better performance in terms of Voltage Transfer Characteristics, noise margin, switching current, inverter gain and propagation delay. The reliability issues of the various devices in terms of supply voltage, temperature and channel length variation has also been studied in the present work. Logic circuits (such as NAND and NOR gate) and ring oscillator are also implemented using different architectures to illustrate the capabilities of ISE-SON architecture for high speed logic circuits as compared to other devices. Results also illustrates that ISE-SON is much more temperature resistant than SON and ISE MOSFET. Hence, ISE-SON enables more aggressive device scaling for low-voltage applications.

• 한국주거학회:학술대회논문집
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• 한국주거학회 2004년도 추계학술대회 논문집
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• pp.335-341
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• 2004

According to the improvements of the education and the cultural level, the noise pollutions which have been occupying a major portion of civil petitions about environment is gradually aggravating. Especially, the plumbing noises which took place at machine room of dormitory are the compositive shapes of an air-borne sounds and a solid-borne sounds. So it has been causing to injure the comfortable residential environment of residents that it is propagated in a residential space. Judging from this point of view, this study grasped the propagation and the properties of attenuation about four varieties's plumbing noise which took place at machine room to understand that it cause influences to a residential space. In this point, we understand the peculiar features by measuring noise, which was generated from equipment in machine rooms of three dormitories having different features. On the basis of these features, we examine all predictability and reliability in comparing the predictive value with the measurable one, using architectural acoustic simulation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 추계학술대회 논문집 전기물성,응용부문
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• pp.9-11
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• 2005

본 연구는 GIS 진단신뢰성 향상기술 개발을 목적으로, 16개의 인위적 결함을 이용하여 부분방전 신호를 발생시키고 검출하여 그 패턴인식 확률을 높이기 위하여 신경망에 Genetic Algorithm (GA) 을 적용하였다. 이를 위하여 다음과 같은 5가지 서로 다른 신경망 모델을 선택하였다: Back Propagation (BP), Jordan-Elman Network (JEN), Principal Component Analysis (PCA), Self-Organizing Feature Map (SOFM) 및 Support Vector Machine (SVM). 이와 같이 선택된 모델에 동일한 데이터를 학습 시키고 패턴인식 확률을 비교 및 분석하였다. 실험 결과에 의하면, BP의 인식률이 가장 높고 다음으로 JEN의 인식률이 높이 나타났으며, 후자의 경우 모든 결함에 대하여 정확한 패턴분류를 한 반면에 전자의 경우 1.8% 의 분류 오차가 발생하였다. 따라서 인식률이 높은 신경망이 더 정확한 패턴분류를 보장하지 못한다는 실험적 결과를 고려 할 때, 인식률이 높은 두 개의 모델을 선정하여 각각의 출력에 일정한 가중치를 주고 합산하여 새로운 출력을 얻는 방법을 제안한다.

• 전기학회논문지
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• 제61권11호
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• pp.1752-1757
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• 2012

With the development in industry, power demand has increased rapidly. As consumption of power has increased, Demand for new power line and electric capacity has risen. However, in the event of fault, problems occur in extending the range of fault coverage and increasing fault current. In these reasons, protection devise is recognized as the prevention of an accident and fault current. This paper dealt with minimizing fault propagation and limiting fault current by adjusting fault current limiter (FCL) with fast interrupter. At this point, we compared and analyzed characteristics between non-inductive resistance and fault current which is limited by superconducting units. In normal state of the power system, power was supplied to the load, but when fault occurred, the interrupter was operated as CT which detected the over-current. Its operation made the limitation of fault current through a FCL. We concluded that the limiter using superconducting units was more efficient with the increase of power voltage. Superconducting fault current limiter with the fast interrupter prevented the spread of a fault, and improved reliability of power system.

• 전기학회논문지
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• 제57권2호
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• pp.155-163
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• 2008

This paper presents a novel framework for analysis of power system wide-area blackout based on so called fault cascading scenarios. For a given power system operating state, "triggering" faults or a "seed faults" are chosen based on the probabilities estimated from the hazard rates. The fault probabilities reflect both the load and the weather conditions. Effects of hidden failures in protection systems are also reflected in establishing the fault propagation scenarios since they are one of the major causes for the wide-area blackouts. A tree type data structure called a PS-BEST(Power System Blackout Event Scenario Tree) is proposed for construction of the fault cascading scenarios, in which nodes represent various power system operating states and the arcs are the events causing transitions between the states. Arcs can be either probabilistic or deterministic. For a given initial fault, the total probability of leading to wide-area blackout is estimated by aggregating the individual probability of each fault sequence route leading to wide-area blackout. A case study is performed on the IEEE RTS-79(24 bus) system based on the fault data presented by the North American Electrical Reliability Council(NERC). Test results demonstrate the potentials and the effectiveness of the proposed technique for the future wide-area blackout analysis.

• Smart Structures and Systems
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• 제13권6호
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• pp.901-925
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• 2014

The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naïve Bayes Classifiers and Bagged Decision Tree.