• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.135-146
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• 1997

This paper describes fault diagnosis method in complex system with hierachical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. Fault diagnosis system can forecast faults in a system and decide from current machine state signal information. Comparing with other diagnosis system for single fault, the developed system deals with multiple fault diagnosis comprising Hierarchical Neural Network(HNN). HNN consists of four level neural network, first level for item fault symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. UNIX IPC(Inter Process Communication) is used for implementing HNN wiht multitasking and message transfer between processes in SUN workstation with X-Windows(Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural newtork operate as a separate process in HNN. The message queue take charge of information exdhange and cooperation between each neural network.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1306-1313
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• 2018

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

• Nuclear Engineering and Technology
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• v.36 no.1
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• pp.73-82
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• 2004

The deaerator of a power plant is one of feedwater heaters in the secondary system, and it is located above the feedwater pumps. The feedwater pumps take the water from the deaerator storage tank, and the net positive suction head(NSPH) should always be ensured. To secure the sufficient NPSH, the deaerator tank is equipped with the level control system of which level sensors are critical items. And it is necessary to ascertain the sensor state on-line. For this, a model-based fault detection and diagnosis(FDD) is introduced in this study. The dynamic control model is formulated from the relation of input-output flow rates and liquid-level of the deaerator storage tank. Then an adaptive state estimator is designed for the fault detection and diagnosis of sensors. The performance and effectiveness of the proposed FDD scheme are evaluated by applying the operation data of Yonggwang Units 3 & 4.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.5 s.110
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• pp.495-502
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• 2006

A fault diagnosis method is developed in this study using automaton through quantization of vibration signals for normal and faulty conditions, respectively. Automaton is a kind of qualitative model which describes the system behaviour at the level of abstraction. The system behavior was extracted from the probability of the output sequence of vibration signals. The sequence was made as vibration levels by reconstructing the originally measured vibration signals. As an example, a fault diagnosis for the bearing of ATM machine was done, which detected the bearing fault with confident level compared to any other existing methods of kurtosis or spectrum analysis.

• Journal of Korean society of Dental Hygiene
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• v.13 no.3
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• pp.525-534
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• 2013

Objectives : This study aimed to improve school health program by investigation of several variables through educational diagnostic factors which influence the level of subjective oral health perception and DMFT of students on the basis of PRECEDE model. Methods : A total of 286 high school students in Busan completed the self-reported questionnaire from September 3 to 28 in 2012. Results : 1. Social and epidemiologic diagnosis suggested that the level of subjective oral health perception of male students was not better than that of female students and DMFT number of the male was more than that of the female(p<0.001)(p<0.001). 2. Oral health diagnosis indicated that once a day tooth brushing group showed lower level of oral health perception(p<0.001) and high DMFT number(p<0.001). 3. Predisposing factor of educational diagnosis implied that more than 4 times a day snack intake group and sweet diet and soda friendly group showed lower level of oral health perception and high DMFT number(p<0.001). 4. Tooth brushing of the reinforcing factors had the most important effect on the level of oral health perception and the number of dental caries. Daily snack intake was the most important effect on DMFT number. Conclusions : The informed consent from each family was the important factor in implementing PRECEDE model. School health program improved oral health care. Oral health program can correct the risk oral health behavior in children and adolescents.

• The Journal of Korean Medicine
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• v.41 no.3
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• pp.1-8
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• 2020

Objectives: This paper aims to investigate the Donguibogam-based pattern diagnosis by applying natural language processing and machine learning. Methods: A database has been constructed by gathering symptoms and pattern diagnosis from Donguibogam. The symptom sentences were tokenized with nouns, verbs, and adjectives with natural language processing tool. To apply symptom sentences into machine learning, Word2Vec model has been established for converting words into numeric vectors. Using the pair of symptom's vector and pattern diagnosis, a pattern prediction model has been trained through Logistic Regression. Results: The Word2Vec model's maximum performance was obtained by optimizing Word2Vec's primary parameters -the number of iterations, the vector's dimensions, and window size. The obtained pattern diagnosis regression model showed 75% (chance level 16.7%) accuracy for the prediction of Six-Qi pattern diagnosis. Conclusions: In this study, we developed pattern diagnosis prediction model based on the symptom and pattern diagnosis from Donguibogam. The prediction accuracy could be increased by the collection of data through future expansions of oriental medicine classics.

• Journal of Digital Convergence
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• v.9 no.2
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• pp.141-152
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• 2011

The data center has become an increasingly important part of most business operations. An increasing demand for computation has led to increasing industry energy consumption. Therefore, higher-than-normal rates of energy efficiency have become a core issue in the life cycle of data center. In this paper, we proposed the framework of level diagnosis for green data centre that can be used to diagnose the levels of capability maturity model. This framework contains the 5 key areas such as construction, air-conditioning, electricity, information technology, organization and indicators that can be applied as basic level diagnosis guide for green data center.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.536-564
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• 2019

Soft faults are inherent in wireless sensor networks (WSNs) due to external and internal errors. The failure of processes in a protocol stack are caused by errors on various layers. In this work, impact of errors and channel misbehavior on process execution is investigated to provide an error classification mechanism. Considering implementation of WSN protocol stack, inter-process correlations of stacked and peer layer processes are modeled. The proposed model is realized through local and global decision trees for fault diagnosis. A hybrid framework is proposed to implement local decision tree on sensor nodes and global decision tree on diagnostic cluster head. Local decision tree is employed to diagnose critical failures due to errors in stacked processes at node level. Global decision tree, diagnoses critical failures due to errors in peer layer processes at network level. The proposed model has been analyzed using fault tree analysis. The framework implementation has been done in Castalia. Simulation results validate the inter-process correlation model-based fault diagnosis. The hybrid framework distributes processing load on sensor nodes and diagnostic cluster head in a decentralized way, reducing communication overhead.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.189-195
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• 2022

The Korean government has made it a major national task to contribute to the revitalization of the creative economy, such as creating new industries and jobs, by encouraging the private opening and utilization of public data. The Korean government is promoting public data quality improvement through activities such as conducting public data quality management level evaluation for high-quality public data retention. However, there is a difference in diagnosis results depending on the understanding and data expertise of users of the public data quality diagnosis tool. Therefore, it is difficult to ensure the accuracy of the diagnosis results. This paper proposes a public data quality management level evaluation domain rule mapping model applicable to validation diagnosis among the data quality diagnosis standards. This increases the stability and accuracy of public data quality diagnosis.

• Journal of Korean society of Dental Hygiene
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• v.11 no.5
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• pp.749-758
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• 2011

Objectives : The purpose of this study was to analyse case reports performed through a dental hygiene process and provide basic data on clinical education of dental hygiene. Methods : 154 case reports which collected for six years were analysed. This study applied dental hygiene process model in dental hygiene diagnosis. Dental hygiene diagnosis was more cleared by dental a hygiene process model. Data analysis was performed by the Frequency statistics using SPSS 12.0 for Windows. Results : 1. The clients are mainly comprised 20's university student(91.9%). 2. In assessment phase, clients finished 100% test of subjective data. 3. When applied a dental hygiene process model in dental hygiene diagnosis, students have identified 23 type of dental hygiene problem and analysed dental hygiene problem frequently used as bleeding of gingiva, calculus and deposit of dental plaque. 4. In case of plan of dental hygiene intervention, Fluoride application showed the most high level(98.1%) in clinical intervention. 5. Results of intervention showed that performance rate(98.7%) of scaling is the most high level. Conclusions : Dental hygiene process model is more useful than other diagnostic models in clinical practice based on dental hygiene process.