• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.229-231
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• 2006

NLPCA(Nonlinear Principal Component Analysis) is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA operates by a feedforward neural network called AANN(Auto Associative Neural Network) which performs the identity mapping. In this work, a sensor fault detection system based on NLPCA is presented. To verify its applicability, simulation study on the data supplied from sensor network is executed.

• 한국해양학회지:바다
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• 제11권2호
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• pp.92-96
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• 2006

비선형 주성분 분석은 기존에 널리 알려져 있는 주성분 분석기법과 유사한 다변수 데이터 분석을 위한 새로운 접근 방법이다. 비선형 주성분 분석은 AANN(Auto Associative Neural Network)으로 PCA와 마찬가지로 변수들 간에 존재하는 상관관계를 제거함으로써 고차의 다변수 데이터를 정보의 손실을 최소화하면서 최소 차원의 데이터로 변환하는 기법이다. AANN기반 센서 고장 검출 기법을 실제 방재시스템에 적용하여 봄으로써 센서 드리프트 등과 같은 센서 고장의 검출 및 유효한 센서 보정 성능을 확인하였다.

• 제어로봇시스템학회논문지
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• 제10권6호
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• pp.545-550
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• 2004

Recent natural disasters like flooding and slope collapse have shown the need for natural risk management system, as they endanger directly public health and cause severe damages on the national economy. In order to improve the efficiency of risk management systems, this management system based on AANN(Auto-Associative Neural Network)is proposed in this paper. AANN can be effectively used for identification of abnormal data and data compression. The proposed AANN-based risk management system collects and stores measurement data from sensors and transmits them to remote server for web-monitoring. Generally, it is desirable to transmit the compressed data instead of raw data in normal state. However, if dangerous situation happens, rapid tramission of measurement data should be required. These requirements are easily satisfied by using AANN. In order to verify the feasibilities of the proposed system, The AANN-based risk management system is applied to slope collapse monitoring system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.385-388
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• 2006

비선형 주성분 분석은 기존에 널리 알려져 있는 주성분 분석기법과 유사한 다변수 데이터 분석을 위한 새로운 접근 방법이다. 비선형 주성분 분석은 AANN(Auto Associative Neural Network)으로 PCA와 마찬가지로 변수들 간에 존재하는 상관관계를 제거함으로써 고차의 다변수 데이터를 정보의 손실을 최소화하면서 최소 차원의 데이터로 변환하는 기법이다. AANN 기반 센서노드 고장검출 기법을 실제 센서 네트워크에 적용하여 봄으로써 센서 드리프트 등과 같은 센서 고장의 검출 및 유효한 센서 보정 성능을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2268-2271
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• 2002

NLPCA(Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the weil-known method of principal component analysis. NLPCA operates by a feedforward neural network called AANN(AutoAssociative Neural Network) which performs the identity mapping. In this work, a sensor fault defection system based on NLPCA is presented. To verify its applicability, simulation study on the data supplied from Saemangeum measurement stations is executed.

• 전기학회논문지P
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• 제56권1호
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• pp.28-36
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• 2007

Sensor networks are usually composed of tens or thousands of tiny devices with limited resources. Because of their limited resources, many researchers have studied on the energy management in the WSNs(Wireless Sensor Networks), especially taking into account communications efficiency. For effective data transmission and sensor fault detection in sensor network environment, a new remote monitoring system based on PCA(Principle Component Analysis) and AANN(Auto Associative Neural Network) is proposed. PCA and AANN have emerged as a useful tool for data compression and identification of abnormal data. Proposed system can be effectively applied to sensor network working in LEA2C(Low Energy Adaptive Connectionist Clustering) routing algorithms. To verify its applicability, some simulation studies on the data obtained from real WSNs are executed.

• Nuclear Engineering and Technology
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• 제42권2호
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• pp.219-230
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• 2010

In nuclear power plants (NPPs), periodic sensor calibrations are required to assure that sensors are operating correctly. By checking the sensor's operating status at every fuel outage, faulty sensors may remain undetected for periods of up to 24 months. Moreover, typically, only a few faulty sensors are found to be calibrated. For the safe operation of NPP and the reduction of unnecessary calibration, on-line instrument calibration monitoring is needed. In this study, principal component-based auto-associative support vector regression (PCSVR) using response surface methodology (RSM) is proposed for the sensor signal validation of NPPs. This paper describes the design of a PCSVR-based sensor validation system for a power generation system. RSM is employed to determine the optimal values of SVR hyperparameters and is compared to the genetic algorithm (GA). The proposed PCSVR model is confirmed with the actual plant data of Kori Nuclear Power Plant Unit 3 and is compared with the Auto-Associative support vector regression (AASVR) and the auto-associative neural network (AANN) model. The auto-sensitivity of AASVR is improved by around six times by using a PCA, resulting in good detection of sensor drift. Compared to AANN, accuracy and cross-sensitivity are better while the auto-sensitivity is almost the same. Meanwhile, the proposed RSM for the optimization of the PCSVR algorithm performs even better in terms of accuracy, auto-sensitivity, and averaged maximum error, except in averaged RMS error, and this method is much more time efficient compared to the conventional GA method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2289-2291
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• 2003

NLPCA(Nonlinear Principal Component Analysis is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA can be implemented by a feedforward neural network called AANN (AutoAssociative Neural Network) which performs the identity mapping. In this work, a sensor fault detection system based on NLPCA and Maximum Likelihood Estimation scheme is presented. To verify its applicability, simulation study on the data supplied from Saemangeum measurement stations is executed.