• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.599-605
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• 2018

Fault diagnosis depends critically on the selection of sensors monitoring crucial process variables. Boolean network (BN) is composed of nodes and directed edges, where the node state is quantized to the Boolean values of True or False and is determined by the logical functions of the network parameters and the states of other nodes with edges directed to this node. Since BN can describe the fault propagation in a sensor network, it can be applied to propose sensor selection strategy for fault diagnosis. In this article, a sufficient condition for parameter identifiability of BN is first proposed, based on which the sufficient condition for fault identifiability of a sensor network is given. Then, the fault identifiability condition induces a sensor selection strategy for sensor selection. Finally, the theoretical result is applied to the fault diagnosis-oriented sensor selection for a nuclear heating reactor plant, and both the numerical computation and simulation results verify the feasibility of the newly built BN-based sensor selection strategy.

• Journal of Sensor Science and Technology
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• v.26 no.6
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• pp.379-385
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• 2017

In this study, an amperometric non-enzymatic glucose sensor was developed on the surface of a glassy carbon electrode by simply drop-casting the synthesized homogeneous suspension of hexagonal boron nitride (h-BN) nanosheets with a copper metal-organic framework (Cu-MOF) composite. Comprehensive analytical methods, including field-emission scanning electron microscopy (FE-SEM), Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), cyclic voltammetry, electrochemical impedance spectroscopy, and amperometry, were used to investigate the surface and electrochemical characteristics of the h-BN-Cu-MOF composite. The FE-SEM, FT-IR, and XRD results showed that the h-BN-Cu-MOF composite was formed successfully and exhibited a good porous structure. The electrochemical results showed a sensor sensitivity of $18.1{\mu}A{\mu}M^{-1}cm^{-2}$ with a dynamic linearity range of $10-900{\mu}M$ glucose and a detection limit of $5.5{\mu}M$ glucose with a rapid turnaround time (less than 2 min). Additionally, the developed sensor exhibited satisfactory anti-interference ability against dopamine, ascorbic acid, uric acid, urea, and nitrate, and thus, can be applied to the design and development of non-enzymatic glucose sensors.

• Nuclear Engineering and Technology
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• v.47 no.7
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• pp.804-813
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• 2015

The electrical-impedance method has been widely used for void-fraction measurement in two-phase flow due to its many favorable features. In the impedance method, the response characteristics of the electrical signal heavily depend upon flow pattern, as well as phasic volume. Thus, information on the flow pattern should be given for reliable void-fraction measurement. This study proposes an improved electrical-conductance sensor composed of a three-electrode set of adjacent and opposite electrodes. In the proposed sensor, conductance readings are directly converted into the flow pattern through a specified criterion and are consecutively used to estimate the corresponding void fraction. Since the flow pattern and the void fraction are evaluated by reading conductance measurements, complexity of data processing can be significantly reduced and real-time information provided. Before actual applications, several numerical calculations are performed to optimize electrode and insulator sizes, and optimal design is verified by static experiments. Finally, the proposed sensor is applied for air-water two-phase flow in a horizontal loop with a 40-mm inner diameter and a 5-m length, and its measurement results are compared with those of a wire-mesh sensor.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.814-826
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• 2023

Sensor faults in nuclear power plant instrumentation have the potential to spread negative effects from wrong signals that can cause an accident misdiagnosis by plant operators. To detect sensor faults and make accurate accident diagnoses, prior studies have developed a supervised learning-based sensor fault detection model and an accident diagnosis model with faulty sensor isolation. Even though the developed neural network models demonstrated satisfactory performance, their diagnosis performance should be reevaluated considering real-time connection. When operating in real-time, the diagnosis model is expected to indiscriminately accept fault data before receiving delayed fault information transferred from the previous fault detection model. The uncertainty of neural networks can also have a significant impact following the sensor fault features. In the present work, a pilot study was conducted to connect two models and observe actual outcomes from a real-time application with an integrated system. While the initial results showed an overall successful diagnosis, some issues were observed. To recover the diagnosis performance degradations, additive logics were applied to minimize the diagnosis failures that were not observed in the previous validations of the separate models. The results of a case study were then analyzed in terms of the real-time diagnosis outputs that plant operators would actually face in an emergency situation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.444-455
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• 2019

The purpose of this research was to analyze technological trends of 「Military Small Giant Venture Tech-Fi Net」 from Defense Technology Information Service via social network analysis. 「Military Small Giant Venture Tech-Fi Net」, which was constituted for their fine technology for application to the military field, registered 847 technologies of 388 companies. In this research, we analyzed 847 technologies for the relations between "Military System" and "Military Technology Category" via centrality measurement, one of the social network analysis methods. The results indicate that the major technologies of domestic military small giant venture companies were "Sensor" and "ICT" for "C4I System" and "Surveillance and Reconnaissance System" and "Platform/Structure" for "Land System", "Aeronautical System" and "Naval Sea System". In contrast, we recognized inadequate technologies, such as "Propellant" and "Material" for "Missile and Ammunition system" and "Sensor" and "ICT" for "Defense System", We hope that our results and method will be conducive to the technological development of Small Giant Venture companies.

• Proceedings of the KSRS Conference
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• v.2
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• pp.997-1000
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• 2006

We estimate the net heat flux in the tropical and subtropical Atlantic Ocean using satellite data. These fluxes are related to changes in sea surface temperature (SST). This variable influences atmospheric circulations and is indicative of surface and subsurface oceanic circulations. We employ data from the geostationary METEOSAT-7 and 8 satellites and from the Special Sensor Microwave/Imager (SSM/I) for the shortwave and long-wave radiative fluxes, and for estimates of SST. For turbulent flux calculations, we use the bulk aerodynamic method with satellite estimates for wind speed and atmospheric humidity and temperature.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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• pp.566-573
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• 2004

This paper describes the ubiquitous home network (uHome-net) testbed and LonRF intelligent devices based on LonWorks technology. These devices consist of Neuron Chip, RF transceiver, sensor, and other peripheral components. Using LonRF devices, a home control network can be simplified and most devices can be operated on LonWorks control network. Also, Indoor Positioning System (IPS) that can serve various location based services was implemented in uHome-net. Smart Badge of IPS, that is a special LonRF device, can measure the 3D location of objects in the indoor environment. In the uHome-net testbed, remote control service, cooking help service, wireless remote metering service, baby monitoring service and security ＆ fire prevention service were realized. This research shows the vision of the ubiquitous home network that will be emerged in the near future.

• Journal of Korean Society on Water Environment
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• v.34 no.6
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• pp.579-590
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• 2018

The lakes' metabolism bears important information for the assessment of the carbon budget due to the accumulation or loss of carbon in the lake as well as the dynamics of the food webs through primary production. A lake-scale metabolism is evaluated by Gross Primary Production (GPP), Ecosystem Respiration (R), and Net Ecosystem Production (NEP), which is the difference between the first two values. Methods for estimating GPP and R are based on the levels carbon and oxygen. Estimation of carbon is expensive because of the use of radioactive materials which requires a high degree of proficiency. The purpose of this study was to estimate Lake Daecheong ecosystem metabolism using high frequency water temperature data and DO measurement sensor, widely utilized in the field of water quality monitoring, and to evaluate the possibility of using the application method. High frequency data was collected at intervals of 10 minutes from September to December 2017 by installing a thermistor chain and a DO sensor in downstream of Daechung Dam. The data was then used to estimate GPP, R and NEP using the R public program LakeMetabolizer, and other metabolism models (mle, ols, kalman, bookkeep). Calculations of gas exchange coefficient methods (cole, crusius, heiskanen, macIntyre, read, soloviev, vachon) were compared. According to the result, Lake Daecheong has some deviation based on the application method, but it was generally estimated that the NEP value is negative and acts as a source of atmospheric carbon in a heterotrophic system. Although the high frequency sensor data used in this study had negative and positive GPP and R values during the physical mixing process, they can be used to monitor real-time metabolic changes in the ecosystem if these problems are solved.

• Nuclear Engineering and Technology
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• v.39 no.2
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• pp.133-142
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• 2007

The Electric Power Research Institute (EPRI) demonstrated a method for monitoring the performance of instrument channels in Topical Report (TR) 104965, 'On-Line Monitoring of Instrument Channel Performance.' This paper presents the results of several models originally developed by EPRI to monitor three nuclear plant sensor sets: Pressurizer Level, Reactor Protection System (RPS) Loop A, and Reactor Coolant System (RCS) Loop A Steam Generator (SG) Level. The sensor sets investigated include one redundant sensor model and two non-redundant sensor models. Each model employs an Auto-Associative Kernel Regression (AAKR) model architecture to predict correct sensor behavior. Performance of each of the developed models is evaluated using four metrics: accuracy, auto-sensitivity, cross-sensitivity, and newly developed Error Uncertainty Limit Monitoring (EULM) detectability. The uncertainty estimate for each model is also calculated through two methods: analytic formulas and Monte Carlo estimation. The uncertainty estimates are verified by calculating confidence interval coverages to assure that 95% of the measured data fall within the confidence intervals. The model performance evaluation identified the Pressurizer Level model as acceptable for on-line monitoring (OLM) implementation. The other two models, RPS Loop A and RCS Loop A SG Level, highlight two common problems that occur in model development and evaluation, namely faulty data and poor signal selection

• Spatial Information Research
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• v.19 no.1
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• pp.127-133
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• 2011

Recently, with the advances in sensor techniques and net work computing, Ubiquitous Sensor Network (USN) has been received a lot of attentions from various communities. The sensor nodes distributed in the sensor network tend to continuously generate a large amount of data, which is called stream data. Sensor stream data arrives in an online manner so that it is characterized as high-speed, real-time and unbounded and it requires fast data processing to get the up-to-date results. The data stream has many application domains such as traffic analysis, physical distribution, U-healthcare and so on. Therefore, there is an overwhelming need of a USN middleware for processing such online stream data to provide corresponding services to diverse applications. In this paper, we propose a novel USN middleware which can provide users both context-aware service and meaningful sequential patterns. Our proposed USN middleware is mainly focused on location based applications which use stream location data. We also show the implementation of our proposed USN middleware. By using the proposed USN middleware, we can save the developing cost of providing context aware services and stream sequential patterns mainly in location based applications.