• Korean Journal of Adult Nursing
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• v.23 no.2
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• pp.180-188
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• 2011

Purpose: This study was to identify nursing diagnosis-outcome-intervention (NANDA- NOC-NIC: NNN) linkages applied to inpatients in general surgical nursing units. Methods: We developed the NNN linkage computerized nursing process program, which consisted of the 107 nursing outcomes and the 190 nursing interventions linked to the 39 nursing diagnoses. This program was applied to 324 patients who admitted to those nursing units from July, 2004 to February, 2005. Results: First, nursing outcomes of each nursing diagnosis were identified as follows: for 'acute pain', pain control, pain level, and comfort level; for 'risk for infection', wound healing: primary intention, wound healing: secondary intention, and infection status; for 'nausea', nutritional status: food & fluid intake, comfort level, symptom severity and hydration. Second, major nursing interventions for each nursing outcome were analyzed as follows: for pain control or comfort level, pain management and medication management; for pain level, pain management and analgesic administration; for wound healing: primary intention, incision site care and wound care; for Wound healing: secondary intention or infection status, infection control; for nutritional status: food & fluid intake, fluid monitoring; for comfort level, nausea management; for symptom severity, nausea management and vomiting management; for hydration, fluid/electrolyte management. Conclusion: This identified NNN linkages will facilitate the use of nursing process in surgical nursing practice and documentation systems.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.569-575
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• 2022

This is a research thesis on the development of a monitoring and diagnosis device that prevents the risk of an accident through monitoring and diagnosis of a modular Main Circuit Breaker (MCB) using Vacuum Interrupter (VI) for Korean high-speed rolling stock. In this paper, a comprehensive MCB monitoring and diagnosis was performed by converging vacuum level diagnosis of interrupter, operating coil monitoring of MCB and environmental temperature/humidity monitoring of modular box. In addition, to develop an algorithm that is expected to have a similar data processing before the actual field test of the MCB monitoring and diagnosis device in 2023, the cluster analysis and factor analysis were performed using the WEKA data mining technique on the big data of Korean railroad transformer, which was previously researched by Tae Hee Evolution with KORAIL.

• Journal of Korean Neurosurgical Society
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• v.59 no.6
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• pp.590-596
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• 2016

Objective : Rheumatoid arthritis (RA) is a systemic disease that can affect the cervical spine, especially the atlantoaxial region. The present study evaluated the risk factors for atlantoaxial subluxation (AAS) development and progression in patients who have undergone surgical treatment. Methods : We retrospectively analyzed the data of 62 patients with RA and surgically treated AAS between 2002 and 2015. Additionally, we identified 62 patients as controls using propensity score matching of sex and age among 12667 RA patients from a rheumatology registry between 2007 and 2015. We extracted patient data, including sex, age at diagnosis, age at surgery, disease duration, radiographic hand joint changes, and history of methotrexate use, and laboratory data, including presence of rheumatoid factor and the C-reactive protein (CRP) level. Results : The mean patient age at diagnosis was 38.0 years. The mean time interval between RA diagnosis and AAS surgery was $13.6{\pm}7.0$ years. The risk factors for surgically treated AAS development were the serum CRP level (p=0.005) and radiographic hand joint erosion (p=0.009). The risk factors for AAS progression were a short time interval between RA diagnosis and radiographic hand joint erosion (p<0.001) and young age at RA diagnosis (p=0.04). Conclusion : The CRP level at RA diagnosis and a short time interval between RA diagnosis and radiographic hand joint erosion might be risk factors for surgically treated AAS development in RA patients. Additionally, a short time interval between RA diagnosis and radiographic hand joint erosion and young age at RA diagnosis might be risk factors for AAS progression.

• 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.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.271-290
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• 2013

The acceleration information is significant for the structural health monitoring, which is the basic measurement to identify structural dynamic characteristics and structural vibration. The efficiency of the accelerometer is subsequently important for the structural health monitoring. In this paper, the distance measure matrix and the support level matrix are constructed firstly and the synthesized support level and the fusion method are given subsequently. Furthermore, the synthesized support level can be served as the determination for diagnosis on accelerometers, while the consensus data fusion method can be used to recover the acceleration information in frequency domain. The acceleration acquisition measurements from the accelerometers located on the real structure National Aquatics Center are used to be the basic simulation data here. By calculating two groups of accelerometers, the validation and stability of diagnosis and recovering on acceleration based on the data fusion are proofed in the paper.

• Journal of Preventive Medicine and Public Health
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• v.43 no.5
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• pp.451-454
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• 2010

Objectives: The American Diabetes Association (ADA) has recently recommended the HbA1c assay as one of four options for making the diagnosis of diabetes mellitus, with a cut-point of $\geq$ 6.5%. We compared the HbA1c assay and the fasting plasma glucose level for making the diagnosis of diabetes among Korean adults. Methods: We analyzed 8710 adults (age 45-74 years), who were not diagnosed as having diabetes mellitus, from the Namwon study population. A fasting plasma glucose level of $\geq$126 mg/dL and an A1c of $\geq$ 6.5% were used for the diagnosis of diabetes. The kappa index of agreement was calculated to measure the agreement between the diagnosis based on the fasting plasma glucose level and the HbA1c. Results: The kappa index of agreement between the fasting plasma glucose level and HbA1c was 0.50. Conclusions: The agreement between the fasting plasma glucose and HbA1c for the diagnosis of diabetes was moderate for Korean adults.

• Journal of Korean Society for Quality Management
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• v.38 no.2
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• pp.150-160
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• 2010

Statistics Korea has been diagnosis national statistics every year since 2006. They diagnosis over 200 kinds of national statistics. They have 7 quality dimension used for quality diagnosis. That is relevance, accuracy, Timeliness, Comparability, Coherence, Accessibility. Since we are interest in how well they produce national statistics, comparability has become the most important dimension recently. In this reason, Statistics Korea try to rating quality level and development comparability index for national statistics. This paper propose the practical method of grading the quality level and developing the comparability index of the national statistics.

• Journal of the Korean Society of Safety
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• v.18 no.1
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• pp.50-55
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• 2003

This paper presents an intelligent computer system, which can easily diagnose electrical fire causes, without the help of human experts of electrical fires diagnosis. For this system, a database is built with facts and rules driven from real electrical fires, and an intellectual database system which even a beginner can diagnose fire causes has been developed, named as an Electrical Fire Causes Diagnosis System : EFCDS. The database system has adopted, as an inference engine, a mixed reasoning approach which is constituted with the rule-based reasoning and the case-based reasoning. The system for a reasoning model was implemented using Delphi 3, one of program development tools, and Paradox is used as a database building tool. To verify effectiveness and performance of this newly developed diagnosis system, several simulated fire examples were tested and the causes of fire examples were detected effectively by this system. Additional researches will be needed to decide the minimal significant level of the solution and the weighting level of important factors.

• 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 the Institute of Electronics Engineers of Korea SD
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• v.41 no.5
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• pp.51-60
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• 2004

Due to the improvements in circuit design and manufacturing technique, the complexity of a circuit is growing. Since the complexity of a circuit causes high frequency of faults, it is very important to locate faults for improvement of yield and reduction of production cost. But unfortunately it takes a long time to find sites of defects by e-beam proving if the physical level. A fault diagnosis algorithm in the Sate level has meaning to reduce diagnosis time by limiting fault sites. In this paper, we propose an efficient fault diagnosis algorithm in the logical level. Our method is hybrid fault diagnosis algorithm using a new fault dictionary and additional fault simulation which minimizes memory consumption and simulation time.