• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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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.

• Proceedings of the KIEE Conference
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• 2006.10c
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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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.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.11 no.2
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• pp.92-96
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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 risk management system is executed.

• Proceedings of the KIEE Conference
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• 2002.07d
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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.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.1
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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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• v.42 no.2
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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.

• Proceedings of the KIEE Conference
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• 2003.07d
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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.

• Journal of Korean Clinical Nursing Research
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• v.22 no.3
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• pp.294-304
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• 2016

Purpose: This study was done to develop an evidence-based external ventricular drainage (EVD) nursing practice guideline in order to provide standardized nursing and prevent EVD related complications. Methods: We used the standardized methodology for nursing practice guideline adaptation developed by Korean Hospital Nurses Association for the guideline adaptation process in this study. Results: The newly developed EVD nursing practice guideline was adapted to the American Association of Neuroscience Nurses (AANN)'s clinical practice guideline which is 'Care of the patient undergoing intra-cranial pressure monitoring/external ventricular drainage of lumbar drainage.' There were 61 recommendations documented in the preliminary guideline all evaluated by 9 experts based on acceptability and applicability. The final practice guideline was composed of 3 domains with 57 recommendations. The three domains of nursing were the insertion, maintenance, and removal of the EVD. The number of recommendations in each domain was 8 in EVD insertions, 39 in EVD maintenance, and 10 in EVD removals. Of the 57 recommendations 3.5% were level 1, 31.5% were level 2, and 65% were level 3. Conclusion: The standardized practice guideline can improve nurses' performance and accuracy. It can also be used as the foundation for effective communication between all medical staff.

• Korean Journal of Adult Nursing
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• v.13 no.4
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• pp.517-528
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• 2001

목적: 본 연구의 목적은 미국 아이오와 대학에서 개발된 Nursing Interventions Classification (NIC)을 사용하여 신경기계 병동과 정형외과 병동에 근무하는 간호사들이 가장 빈번히 수행하는 간호중재를 파악함으로서 이러한 부서의 간호중재 리스트를 개발하는데 있다. 그리고 확인된 간호중재의 리스트를 미국 간호협회의 핵심 간호중재 목록과 비교함으로써 양국간의 간호중재의 유사성과 차이를 비교함으로써 한국간호의 발전을 도모하는데 있다. 방법: 8개의3차병원 및 종합병원 근무하는 간호사에게 NIC을 번역하여 소개한 뒤 가장 자주 수행하는 간호중재 30개를 선택하게 하였다. 선택된 간호중재는 빈도와 백분율 이용하여 분석하였고 미국의 핵심간호 중재목록과 영역(domain)별, 분류군 (class)별로 비교하였다. 결과: 신경기계 병동은 30개의 간호중재를 정형외과 병동은 34개를 핵심간호중재로 확인하였다. 한국과 미국의 간호중재를 비교해본 결과 신경기계 병동의 간호중재와 미국American Association of Neuroscience Nurses (AANN) 의 핵심간호중재 목록과는 단지 5개의 간호중재가 일치하였지만 정형외과 병동의 간호중재목록은 미국의 National Association of orthopaedic Nurses (NAON) 의 핵심중재목록과 27개의 간호중재가 일치되어 더 많은 유사성을 나타내었다. 두 나라의 간호중재를 영역(domain)별로 비교해보면 한국 간호사의 간호중재는 미국간호단체의 핵심간호 중재 목록보다 신체적 간호에 보다 많은 편중을 보였다. 결론: 한국간호사의 간호중재가 신체적 간호중재에 집중되어 있으므로 환자간호에 보다 전인적인 간호중재가 수행될 수 있도록 노력해야 할 것이다. 그리고 본 연구를 통해 개발된 핵심 간호중재 목록은 병원의 간호정보시스템을 개발하는데 사용될 수 있으며, 간호지식의 확장이나 staffing, 간호 수가화, 그리고 궁극적으로는 간호의 효과성 연구를 자극할 수 있을 것이다.