• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 추계학술대회논문집
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• pp.311-316
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• 2004

For the location estimation in the conventional LPMS(Loose Parts Monitoring System), it is popular to employ a group delay among the acoustic sensors installed within a 3 ft range from the impact source. However, there exists inherent error in determining the arrival time differences of the generated wave group among the neighboring sensors. To overcome this problem in this study, the two dimensional approach has been proposed and applied to effectively estimate the arrival time differences by using a continuous wavelet transform which is one of the linear time-frequency analysis methods. The experiment has been performed to both the plate model and the real steam generator in a nuclear power plant. It is expected that the reliability of the location estimation could be enhanced when the proposed time-frequency method is introduced into the LPMS system.

• 응용통계연구
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• 제7권2호
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• pp.225-237
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• 1994

본 연구에서는 핵확산 감시와 관련된 이상점 탐지를 위한 일반화 우도비 검정 방법이 개발되었다. 고전적인 이상점 탐지방법들이 연속형 변수만을 고려한 반면, 본 연구에서 제안된 방법은 연속형 변수, 이산형 변수, 혹은 이산형과 연속형이 혼합된 변수들에 모두 적용될 수 있다. 더우기 대부분의 고전적인 방법들에 있어서 주로 이용된 정규분포 가정을 필요로 하지 않는다. 본 연구에서 제안된 방법은 일반화 우도비에 붓스트랩 방법을 적용하여 구성되었다. 모의 실험을 통하여 검정력을 고찰함으로써 제안된 검정방법의 성능을 연구하였다.

• 한국해양공학회지
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• 제25권4호
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• pp.59-65
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• 2011

In structural health monitoring (SHM) using electro-mechanical impedance signatures, it is a critical issue for extremely large structures to extract the best damage diagnosis results, while minimizing unknown environmental effects, including temperature, humidity, and acoustic vibration. If the impedance signatures fluctuate because of these factors, these fluctuations should be eliminated because they might hide the characteristics of the host structural damages. This paper presents a long-term SHM technique under an unknown noisy environment for tidal current power plant structures. The obtained impedance signatures contained significant variations during the measurements, especially in the audio frequency range. To eliminate these variations, a continuous principal component analysis was applied, and the results were compared with the conventional approach using the RMSD (Root Mean Square Deviation) and CC (Cross-correlation Coefficient) damage indices. Finally, it was found that this approach could be effectively used for long-term SHM in noisy environments.

• Smart Structures and Systems
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• 제30권3호
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• pp.273-286
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• 2022

In recent years, vision-based monitoring has received great attention. However, structural identification using vision-based displacement measurements is far less established. Especially, simultaneous identification of structural systems and unknown excitation using vision-based displacement measurements is still a challenging task since the unknown excitations do not appear directly in the observation equations. Moreover, measurement accuracy deteriorates over a wider field of view by vision-based monitoring, so, only a portion of the structure is measured instead of targeting a whole structure when using monocular vision. In this paper, the identification of structural system and excitations using vision-based displacement measurements is investigated. It is based on substructure identification approach to treat of problem of limited field of view of vision-based monitoring. For the identification of a target substructure, substructure interaction forces are treated as unknown inputs. A smoothing extended Kalman filter with unknown inputs without direct feedthrough is proposed for the simultaneous identification of substructure and unknown inputs using vision-based displacement measurements. The smoothing makes the identification robust to measurement noises. The proposed algorithm is first validated by the identification of a three-span continuous beam bridge under an impact load. Then, it is investigated by the more difficult identification of a frame and unknown wind excitation. Both examples validate the good performances of the proposed method.

• Smart Structures and Systems
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• 제5권2호
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• pp.139-152
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• 2009

Globally, civil infrastructures are deteriorating at an alarming rate caused by overuse, overloading, aging, damage or failure due to natural or man-made hazards. With such a vast network of deteriorating infrastructure, there is a growing interest in continuous monitoring technologies. In order to provide a true distributed sensor and control system for civil structures, we are developing a Structural Nervous System that mimics key attributes of a human nervous system. This nervous system is made up of building blocks that are designed based on mechanoreceptors as a fundamentally new approach for the development of a structural health monitoring and diagnostic system that utilizes the recently developed piezo-fibers capable of sensing and actuation. In particular, our research has been focused on producing a sensory nervous system for civil structures by using piezo-fibers as sensory receptors, nerve fibers, neuronal pools, and spinocervical tract to the nodal and central processing units. This paper presents up to date results of our research, including the design and analysis of the structural nervous system.

• Smart Structures and Systems
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• 제7권2호
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• pp.133-152
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• 2011

System identification is a fundamental step towards the application of structural health monitoring and damage detection techniques. On this respect, the development of evolved identification strategies is a priority for obtaining reliable and repeatable baseline modal parameters of an undamaged structure to be adopted as references for future structural health assessments. The paper presents the identification of the modal parameters of the Guangzhou New Television Tower, China, using a data-driven stochastic subspace identification (SSI-data) approach complemented with an appropriate automatic mode selection strategy which proved to be successful in previous literature studies. This well-known approach is based on a clustering technique which is adopted to discriminate structural modes from spurious noise ones. The method is applied to the acceleration measurements made available within the task I of the ANCRiSST benchmark problem, which cover 24 hours of continuous monitoring of the structural response under ambient excitation. These records are then subdivided into a convenient number of data sets and the variability of modal parameter estimates with ambient temperature and mean wind velocity are pointed out. Both 10 minutes and 1 hour long records are considered for this purpose. A comparison with finite element model predictions is finally carried out, using the structural matrices provided within the benchmark, in order to check that all the structural modes contained in the considered frequency interval are effectively identified via SSI-data.

• 한국정보통신학회논문지
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• 제19권12호
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• pp.2911-2916
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• 2015

본 논문에서는 기상 제약이 많은 CCTV 보완을 위해 CW 도플러 레이더를 사용하는 감시 시스템에서 두 개 이상의 물체 접근 또는 이탈 정보를 감지하기 위한 모니터링 프로그램을 개발하였으며, 물체의 접근 또는 이탈 정보를 효율적으로 감지하기 위한 알고리즘을 제안하였다. 제안하는 알고리즘은 CW 도플러 레이더에서 수신한 신호를 FFT 처리하여 실수부와 허수부로 분리 후 각 동일 주파수 마다 진폭을 합산하여 이들의 위치를 통해 접근 또는 이탈을 판단하는 방식이다. 이를 시뮬레이션 및 실험을 통해 검증하였으며, 두 개 물체의 접근 또는 이탈을 감지한 결과 정상적으로 감지하는 것을 확인할 수 있었다.

• 항공우주정책ㆍ법학회지
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• 제28권1호
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• pp.73-96
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• 2013

ICAO는 기존의 항공안전평가제도를 개선한 항공안전평가제도(USOAP)를 1995년 도입하였다. 이는 최근의 항공기 사고율 감소에 효과적인 역할을 하고 있다고 평가되고 있으며 이러한 성과를 바탕으로 2013년부터는 기존의 '스냅샷 방식'에서 '상시 모니터링 방식(USOAP-Continuous Monitoring Approach)'으로 전환하였다. ICAO 항공안전평가 결과는 오늘날 국제사회에서 국가의 '항공안전 신인도'를 판단하는 객관적인 지표로써 항공산업에 막대한 영향을 미치고 있으며, 특히 평가결과가 미흡할 경우엔 항공노선 확충 및 코드쉐어 금지, 환승객 감소 국제항공 비즈니스 및 보험료 인상 등 경제적 불이익을 감수해야 한다. 또한, ICAO는 기존의 법규이행(Prescriptive Approach)을 기본으로 하되 리스크 기반(Risk-based) 사전예방형(Proactive Approach) 항공안전시스템 개념을 새로 도입하는 등 국제항공안전정책 패러다임을 전환하여 왔다. ICAO가 새로운 국제항공안전정책을 추진함에 따라 대한민국 정부도 이에 발맞춰 국내항공안전정책을 변화시켜 왔다. 특히, 시스템적 안전관리를 위해 기존의 법규이행 중심의 정부 안전감독시스템에 리스크 기반의 사전 예방적 안전관리 개념을 도입하고 있으며, ICAO 국제기준에 따라 항공서비스제공자에게 자체 안전관리시스템(SMS)를 운영토록 항공법에 규정하였다. 또한, 항공안전의 중심 분야인 항공기 운항 및 정비 분야에 대한 안전 증진을 위해 다양한 안전정책을 추진하고 있다. 이러한 신 국제항공안전정책 패러다임 변화에 발맞춰 국제사회에서 가장 모범적인 항공안전시스템을 갖춘 나라로 확고히 자리 매김하고, 우리 항공의 경쟁력을 튼튼하게 뒷받침한다는 계획이다. 이를 위해서는 정부, 항공사, 연구기관 등 항공관계자가 모두가 노력할 때 실질적인 효과를 기대할 수 있을 것이다.

• 한국소음진동공학회논문집
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• 제13권1호
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• pp.48-55
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• 2003

Hidden Markov Model(HMM) has a doubly embedded stochastic process with an underlying stochastic process that can be observed through another set of stochastic processes. This structure of HMM is useful for modeling vector sequence that doesn't look like a stochastic process but has a hidden stochastic process. So, HMM approach has become popular in various areas in last decade. The increasing popularity of HMM is based on two facts : rich mathematical structure and proven accuracy on critical application. In this paper, we applied continuous HMM (CHMM) approach with AR coefficient to detect and predict the chatter of lathe bite and to diagnose the wear of oil Journal bearing using rotor shaft displacement. Our examples show that CHMM approach is very efficient method for machine health monitoring and prediction.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.18-18
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• 1996

This paper reviews some of the most prominent and promising areas of chemical process control both in relations to batch and continuous processes. These areas include the modeling, optimization, control and monitoring of chemical processes and entire plants. Most of these areas explicitly utilize a model of the process. For this purpose the types of models used are examined in some detail. These types of models are categorized in knowledge-driven and datadriven classes. In the areas of modeling and optimization, attention is paid to batch reactors using the Tendency Modeling approach. These Tendency models consist of data- and knowledge-driven components and are often called Gray or Hybrid models. In the case of continuous processes, emphasis is placed in the closed-loop identification of a state space model and their use in Model Predictive Control nonlinear processes, such as the Fluidized Catalytic Cracking process. The effective monitoring of multivariate process is examined through the use of statistical charts obtained by the use of Principal Component Analysis (PMC). Static and dynamic charts account for the cross and auto-correlation of the substantial number of variables measured on-line. Centralized and de-centralized chart also aim in isolating the source of process disturbances so that they can be eliminated. Even though significant progress has been made during the last decade, the challenges for the next ten years are substantial. Present progress is strongly influenced by the economical benefits industry is deriving from the use of these advanced techniques. Future progress will be further catalyzed from the harmonious collaboration of University and Industrial researchers.