• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제11권4호
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• pp.1-8
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• 2007

In some wireless sensor networks, the sensor nodes are required to be located sparsely at designated positions over a wide area, introducing the problem of adding minimum number of relay nodes to interconnect the sensor nodes. The problem finds its a bstract form in literature: the Minimum number of Steiner Points. Since it is known to be NP-hard, this paper proposes an approximation scheme to estimate the minimum number of relay nodes through the properties of the abstract form. Note that by reducing the numb er of nodes in a sensor network, the amount of data exchange over the net will be far decreased.

• Nuclear Engineering and Technology
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• 제51권1호
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• pp.146-154
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• 2019

An improved principal component analysis (PCA) method is applied for sensor fault detection and isolation (FDI) in a nuclear power plant (NPP) in this paper. Data pre-processing and false alarm reducing methods are combined with general PCA method to improve the model performance in practice. In data pre-processing, singular points and random fluctuations in the original data are eliminated with various techniques respectively. In fault detecting, a statistics-based method is proposed to reduce the false alarms of $T^2$ and Q statistics. Finally, the effects of the proposed data pre-processing and false alarm reducing techniques are evaluated with sensor measurements from a real NPP. They are proved to be greatly beneficial to the improvement on the reliability and stability of PCA model. Meanwhile various sensor faults are imposed to normal measurements to test the FDI ability of the PCA model. Simulation results show that the proposed PCA model presents favorable performance on the FDI of sensors no matter with major or small failures.

• 정보처리학회논문지C
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• 제16C권3호
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• pp.285-298
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• 2009

유비쿼터스 기술의 발전과 함께 센서 네트워크는 다양한 분야에서 활용되고 있다. 그 중 특히 의료 분야는 중요한 응용 분야 중의 하나로 바디 센서 네트워크의 표준화 동향과 함께 관심이 집중되고 있다. 의료 센서 네트워크는 기존의 일반적인 환경의 센서 네트워크와는 다른 의료 환경만의 특징들을 가지고 있다. 따라서 본 논문에서는 이와 같은 특징들을 반영하여 계층적인 의료 센서 네트워크 구조를 제안하였고, 계층적인 구조를 바탕으로 하여 센싱 데이터 전송 방식을 소개하였다. 즉, 효율적인 센싱 데이터 전송을 위해서는 환자들의 요구 사항과 건강 상태를 고려하여 각 센서 노드들에게 우선 순위(Priority)와 경계값(Threshold Value)을 주었다. 이를 통해 클러스터 헤드에서 응급 데이터를 우선적으로 빠르게 베이스스테이션으로 전송하도록 하였다. 또한 이와 같은 구조와 전송 방식을 바탕으로 센서 네트워크를 위해 Eschenauer와 Gligor가 제안한 키 메커니즘을 기반으로 하여 새로운 키 관리 기법을 제안하였다. 이는 각 클러스터 헤드들이 높은 우선 순위를 갖는 응급 노드들에 대해서 이웃 클러스터로 응급 노드와의 키를 미리 전송해주는 Key Provisioning 방법을 사용하여 응급 노드들에 대해서 키 설립을 준비하도록 하여 키 설립이 보다 빠르게 이루어지도록 하였다. 이를 통해 키 설립 지연으로 인한 데이터 전송의 기다림 없이 바로 응급 노드들의 데이터를 클러스터 헤드로 전송할 수 있도록 한다. 이와 같은 계층적인 구조에서의 데이터 전송 방식과 이를 바탕으로 제안한 키 관리 기법은 수식 및 QualNet 시뮬레이터를 사용한 시뮬레이션을 통하여 네트워크 트래픽 오버헤드와 에너지 소모량을 분석하였으며, TmoteSKY 센서보드를 사용해 구현함으로써 그 효율성을 증명하고 실제 응용환경에서의 실현가능성을 입증하였다.

• 제어로봇시스템학회논문지
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• 제11권8호
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• pp.662-668
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• 2005

This paper presents a realization of check valve condition monitoring system based on fault diagnosis algorithm and Fieldbus communication. We first acquired AE(acoustic emission) sensor data at the check valve test loop, extract fault features through the teamed neural network, and send the processed data to a remote site. The overall system has been implemented and experimented results are given to show its effectiveness.

• 산업경영시스템학회지
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• 제46권3호
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.49-51
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• 2004

This paper presents a realization of fault detection algorithm and Fieldbus based communication for condition monitoring of check valve. We first acquired the AE(Acoustic Emission) sensor data at the KAERI check valve test loop, extract fault features through the learned Neural network, and send the processed data to a remote site. The overall system has been implemented and experimental results are given to show its effectiveness.

• 한국재료학회지
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• 제13권11호
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• pp.732-736
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• 2003

A CP-GFRP(Carbon Powder-Glass Fiber Reinforced Plastic) sensor was fabricated for fracture detection. The electric resistance of the sensor was measured on condition of various composition of carbon powders and thickness of bundle of glass fibers. The resistance of the sensor was decreased as the increase of the content of carbon powders and the TEX of the glass fibers. In the case of loading on CP-GFRP, because inner crack was propagated, the part of percolation structures was disconnected. These observations show the following results. The conduction of CP-GFRP sensor is due to percolation structure of carbon powders and increase of resistance is due to expansion of cracks.

• Nuclear Engineering and Technology
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• 제52권7호
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• pp.1452-1461
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• 2020

A triple bubbler sensor was tested in LiCl-KCl molten salt from 450 to 525 ℃ in a transparent furnace to validate thermal-expansion corrections and provide additional molten salt data sets for calibration and validation of the sensor. In addition to these tests, a model was identified and further developed to accurately determine the density, surface tension, and depth from the measured bubble pressures. A unique feature of the model is that calibration constants can be estimated using independent depth measurements, which allow calibration and validation of the sensor in an electrorefiner where the salt density and surface tension are largely unknown. This model and approach were tested using the current and previous triple bubbler data sets, and results indicate that accuracies are as high as 0.03%, 4.6%, and 0.15% for density, surface tension, and depth, respectively.

• Transactions on Electrical and Electronic Materials
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• 제7권4호
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• pp.184-188
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• 2006

This work describes efforts in the fabrication and testing of robust microelectromechanical systems (MEMS). Robustness is typically achieved by investigating non-silicon substrates and materials for MEMS fabrication. Some of the traditional MEMS fabrication techniques are applicable to robust MEMS, while other techniques are drawn from other technology areas, such as electronic packaging. The fabrication technologies appropriate for robust MEMS are illustrated through laminated polymer membrane based pressure sensor arrays. Each array uses a stainless steel substrate, a laminated polymer film as a suspended movable plate, and a fixed, surface micromachined back electrode of electroplated nickel. Over an applied pressure range from 0 to 34 kPa, the net capacitance change was approximately 0.14 pF. An important attribute of this design is that only the steel substrate and the pressure sensor inlet is exposed to the flow; i.e., the sensor is self-packaged.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2018년도 추계학술발표대회
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• pp.353-355
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• 2018

The aim of this paper is Although a large amount of sensor data is generated in the manufacturing process of a manufacturing company and the necessity of utilizing sensor data is felt, there is a real difficulty in collecting a large amount of sensor data and introducing analysis / service. Using W3C Web document standard format, XML based technology, it collects data to provide production status and equipment status information, and researches middleware for sensor data processing that user can analyze.