• The Journal of Korean Academy of Prosthodontics
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• v.41 no.6
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• pp.714-719
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• 2003

Conical crown-retained denture (CCRD) has been used as a very effective treatment method in cases with few remaining teeth with heterogeneous prognosis. However, in spite of many advantages of CCRD, high technical failure rate was a problem to be considered. Incorrect path of insertion and excessive retention were thought to be the main cause of technical failure and to result from laboratory procedure with a coping misfit and/or a coping transfer error. In order to prevent this error, secure anchoring of inner coping and re-examination and milling of convergence angle were recommended on the master model from pick-up impression.

• Journal of the Korean Society of Safety
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• v.20 no.3 s.71
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• pp.19-26
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• 2005

There are three reasoning method in fault diagnosis process. The shallow reasoning is based on the experiential knowledge and deep reasoning is based on physical model. Hybrid reasoning is mixing two type reasoning. This study describes about fault train embodiment of screw type air compressor that is used widely in industrial facilities by using various experimental method and shallow reasoning. We investigate macroscopic failure cause of air compressor through naked eye observation and then microscopic failure cause by various experimental method. We composed fault train with fault knowledge based on empirical data and scientific data that is acquired through several experiments. It is possible to analysis system reliability and failure rate with these fault train.

• Journal of Korean Society for Quality Management
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• v.39 no.2
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• pp.320-328
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• 2011

This research proposes comprehensive models for analyzing common cause failures (CCF) due to cumulative shocks and to assess system reliability under the CCF. The proposed cumulative shock models are based on the binomial failure rate (BFR) model. Six kinds of models are proposed so as to explain diverse cumulative shock phenomena. The models are composed of the initial failure probability, shape parameter, and the total shock number. Some parameters of the proposed models can not be explicitly estimated, so we adopt the Expectation-maximization (EM) algorithm in order to obtain the maximum likelihood estimator (MLE) for the parameters. By estimating the parameters for the cumulative shock models, the system reliability with CCF can be assessed sequentially according to the number of cumulative shocks. The result can be utilizes in dynamic probabilistic safety assessment (PSA), aging studies, or risk management for nuclear power plants. Replacement or maintenance policies can also be developed based on the proposed model.

• Journal of Chest Surgery
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• v.18 no.4
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• pp.649-654
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• 1985

After first successful mitral valvotomy by Harken and Bailey in 1948, improvement of surgical technique and cardiac device may last rapidly for several years, but there after many patients deteriorate because of various causes, so incidence of reoperation for cardiac valvular disease has increased time by time. This paper is concerned with 21 patients in whom a second operation has been carried out from Jan. 1963 to Aug. 1984 at the department of Thoracic and Cardiovascular department, National Medical Center. Of 21 patients, 7 were male and 14 were female, and ages ranged from 14 to 37 years The second operation are classified into groups of secondary closed mitral commissurotomy [3 cases], open commissurotomy following closed mitral commissurotomy [1 case], Valve replacement following closed mitral commissurotomy [14 cases] or bioprosthetic valve replacement [3 cases]. Main cause of reoperation was restenosis or steno insufficiency, and that of bioprosthetic valve failure was bacterial endocarditis [1 case], fibrous tissue overgrowth on the Xenograft [1 case] and technical failure [1 case]. Early operative mortality was absent, but during follow-up, 4 patients died, so late mortality was 19.0%, and main cause of death was congestive heart failure.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.9
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• pp.105-109
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• 2022

In this study, the cause of the acceleration failure of Hyundai Motor's 2.0-liter CRDi engine was analyzed. We tried to find problems through MDA (Measuring Data Analyzer) based on data such as vehicle speed, air intake, and air-fuel ratio obtained during the actual driving process. As a result, it was analyzed that the failure of the EGR valve exceeded the NOx emission standard and caused a decrease in engine output. Through this study, it is possible to reduce the time and cost of unnecessary maintenance and repair, and it is expected that a rapid cause analysis will be possible in the case of new failure diagnosis in the future.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.357-371
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• 2007

For the time-to-failure data with competing risks, cumulative incidence functions (CIFs) are commonly estimated using nonparametric methods. If the cases of events due to the cause of primary interest are infrequent relative to other cause of failure, nonparametric methods may result in rather imprecise estimates for CIF. In such cases, Bryant et al. (2004) suggested to model the cause-specific hazard of primary interest parametrically, while accounting for the other modes of failure using nonparametric estimator. We represented the semiparametric cumulative incidence estimator and extended to the model of Weibull and log-normal distribution. We also conducted simulations to access the performance of the semiparametric cumulative incidence estimators and to investigate the impact of model misspecification in log-normal cause-specific hazard model.

• IE interfaces
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• v.25 no.4
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• pp.405-415
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• 2012

The reliability performance measures for low and high or continuous demand modes of operation of safety instrumented systems(SISs) are examined and compared by analyzing the official definitions in IEC 61508 standard. This paper also presents a status of common cause factor(CCF) models used in IEC 61508 and problems relating CCF modelling are discussed and ideas to solve these ones are suggested. An example with mixed M-out-of-N architecture is carried out to illustrate the proposed methods.

• Electronics and Telecommunications Trends
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• v.8 no.3
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• pp.39-52
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• 1993

This study involves a Common Cause Failure (CCF) problem on the ultra-high reliability required system development such as war game operations, nuclear power control, air traffic control, space shuttle missions, and large scale network communication system. The system situation problems are defined according to CCF, reliability and system fault identifications for the development cast verifications in the multi-version redundant software system. Then, CCF analysis of redundant system, system principles and statistical dependence are also described. This validation oh the CCF in the human software interaction system will notify software engineers to conceive what really is CCF contribution factor, not only the internal but the external ones.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.64-70
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• 2018

The development of weapon systems (or components) is hindered by the number of tests due to the limited development period and cost, which reduces the scale of accumulated data related to failures. Nevertheless, because a large amount of failure data and maintenance details during the operational period are managed by computerized data, the cause of failure of weapon systems (or components) can be analyzed using the data. On the other hand, analyzing the failure and maintenance details of various weapon systems is difficult because of the variation among groups and companies, and details of the cause of failure are described as unstructured text data. Fortunately, the recent developments of big data processing technology, machine learning algorithm, and improved HW computation ability have supported major research into various methods for processing the above unstructured data. In this paper, unstructured data related to the failure / maintenance of defense weapon systems (or components) is presented by applying doc2vec, a machine learning technique, to analyze the failure cases.

• Quality Improvement in Health Care
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• v.5 no.2
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• pp.190-201
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• 1998

Background : Medical equipments take a very important role in diagnosis and treatment of disease in modern medicine and effective maintenance of the equipments is a necessary to provide a good health care to the public. After developing a new QC program for effective maintenance of medical equipments and practicing it for a year, we report the results of the new program. Methods : The maintenance data of 9 equipments in 8 categories including a CT Scanner were analyzed with regard to the parts responsible for most frequent failure and cause of the failure. After learning the most frequent failure part and cause of the failure, we developed a new QC program that emphasizes preventive maintenance of the most frequent failure part. We compared the number of failure per year and active rate of each equipment before and after the adoption of the new QC program. Results : The average number of failure per year per equipment was 20.7 before and it decreased by 43% to 11.9 after adoption of the new QC program. The average active rate of the equipments was 92.6% before and it increased by 3.2% to 95.8% after adoption of the new program. Conclusions : The practice of the new QC program appears very useful as it decreased the failure rate and increased the active rate of the equipments.