• Smart Structures and Systems
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• v.5 no.2
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• pp.153-171
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• 2009

In this paper, the analytical fault detection and diagnosis (FDD) is presented using model-based and signal-based methodology with wavelet analysis on signals obtained from sensors and sensor networks. In the model-based FDD, we present the modeling of contact interface found in soft materials, including the biomedical contacts. Fingerprint analysis and signal-based FDD are also presented with an experimental framework consisting of a mechanical pneumatic system typically found in manufacturing automation. This diagnosis system focuses on the signal-based approach which employs multi-resolution wavelet decomposition of various sensor signals such as pressure, flow rate, etc., to determine leak configuration. Pattern recognition technique and analytical vectorized maps are developed to diagnose an unknown leakage based on the established FDD information using the affine mapping. Experimental studies and analysis are presented to illustrate the FDD methodology. Both model-based and wavelet-based FDD applied in contact interface and manufacturing automation have implication towards better quality of life by applying theory and practice to understand how effective diagnosis can be made using intelligent FDD. As an illustration, a model-based contact surface technology an benefit the diabetes with the detection of abnormal contact patterns that may result in ulceration if not detected and treated in time, thus, improving the quality of life of the patients. Ultimately, effective diagnosis using FDD with wavelet analysis, whether it is employed in biomedical applications or manufacturing automation, can have impacts on improving our quality of life.

• Journal of the Korean housing association
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• v.26 no.2
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• pp.67-79
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• 2015

As the Korean construction market in the apartment housing has changed to a housing consumer focused market, interest and importance on efficient use and management on existing buildings has increased rather than demand for new buildings. Interest of housing consumers on apartment house quality has increased in this market paradigm, and this spontaneously is connected to quality flaw related defect disputes and lawsuits that the importance of defect diagnosis has continuously increased. This defect diagnosis is directly connected to maintenance charges in defect dispute and lawsuit processes that rather objective and highly credible progress of duty is required. However, most defect diagnosis firms today that progress defect diagnosis are using different diagnosis methods and depend on the experience of experienced professionals that there is no standardized defect diagnosis process. Therefore, the purpose of this study is to provide common defect diagnosis process model for defect diagnosis firms utilizing the BPMN (Business Process Modeling Notation) modeling method. It is expected that this will contribute to professional and reliable task performances of concerned defect diagnosis workers. Furthermore, it is expected that design lawsuit damage will be substantially reduced by standardizing defect diagnosis processes.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.277-279
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• 2005

Recently, due to the increase of power conversion devices and nonlinear loads with the development of information, communication and control technologies, the instantaneous minute interruption factors such as voltage & current harmonics, surge occurring frequency, instantaneous voltage variation, voltage unbalance, flicker etc. have greatly threatened the power quality, and the deterioration of electric power facilities and the functional error of controllers are increasing. As such an instantaneous minute interruption appears to be small and local, accurate evaluation with measurement is difficult and total analysis system is required through a wide range of power quality effect analysis such as the simultaneous measurement on various power supply phenomena and the analysis on the interrelation with system loads. Most of conventional power quality diagnosis equipments have beer developed and applied, which were able to measure the stability rate of frequency, the stability rate of voltage, the electricity-failure duration etc, However, they were insufficient to analyze the system present situation, understand the cause of the failure occurred by the problem of power quality and analyze out the phenomena. Accordingly, this study will address the development of the system for a wide range of power quality diagnosis over the present level, the system for supporting the determination such as the analysis on risk factors, failure mode and impact, the system for harmonic evaluation based on international standards(IEC 61000 Series) and the total power quality diagnosis network & system with the extension and openness as a local and national-scale broadband power quality diagnosis system.

• The Journal of Bigdata
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• v.3 no.2
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• pp.11-18
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• 2018

This study is a study on domain automatic classification for domain - based quality diagnosis which is a key element of big data quality diagnosis. With the increase of the value and utilization of Big Data and the rise of the Fourth Industrial Revolution, the world is making efforts to create new value by utilizing big data in various fields converged with IT such as law, medical, and finance. However, analysis based on low-reliability data results in critical problems in both the process and the result, and it is also difficult to believe that judgments based on the analysis results. Although the need of highly reliable data has also increased, research on the quality of data and its results have been insufficient. The purpose of this study is to shorten the work time to automizing the domain classification work which was performed from manually to using machine learning in the domain - based quality diagnosis, which is a key element of diagnostic evaluation for improving data quality. Extracts information about the characteristics of the data that is stored in the database and identifies the domain, and then featurize it, and automizes the domain classification using machine learning. We will use it for big data quality diagnosis and contribute to quality improvement.

• Journal of Korean Society on Water Environment
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• v.28 no.1
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• pp.18-25
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• 2012

For effective watershed management, it is very important to select the tributaries through selection and concentration of targeted tributary catchments for improving water quality within the limited financial conditions. This study introduced the selection and diagnosis procedure of tributary catchments for improving water quality at Chungcheongnam-do based on water quality and flowrate monitoring, stream grouping method. The tributaries with high value of water quality and flowrate were selected for improving water quality according to stream grouping method. The diagnosis of selected tributaries for improving water quality was performed with analysis of the pollution load (generated, discharged, delivered) and point source discharged pollution load density. The plans for improving water quality of tributaries were suggested thorough various diagnosis of tributary catchments. For improving water quality of tributaries in Chungcheongnam-do, the tributary catchments in the Dangjin, Asan, Yesan, Cheonan, Hongseong area should be preferentially considered. The water quality improving plans for those tributary catchments, in accordance with the reduction of sources of pollution by population and livestock, should be established.

• The Journal of Internal Korean Medicine
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• v.31 no.4
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• pp.880-891
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• 2010

This study is about a comparison of controlled and uncontrolled hypertension groups regarding comprehensive diagnosis of Qi blood water and quality of life. We surveyed "controlled and uncontrolled hypertension patients" using questionnaires for comprehensive diagnosis of Qi blood water, SF-36 and HTN QoL (Measurement Scale for the quality of life in hypertensive patients). There was no difference in comprehensive diagnosis of Qi blood water between the controlled and uncontrolled hypertension groups. Within the controlled hypertension group, the patients diagnosed with a Qi deficiency, Qi stagnation, Qi counterflow, blood deficiency, and water retention received lower total scores in SF-36 and HTN QoL than in undiagnosed patients. Within the uncontrolled hypertension group, the patients diagnosed with Qi deficiency, blood deficiency, and water retention got lower total scores in SF-36 and HTN QoL than in undiagnosed patients. These results were statistically significant. These results are insufficient that we and use comprehensive diagnosis of Qi blood water for a diagnosis tool of hypertension. But if we have better studies that make up for weak points, these results will help to make a diagnosis tool for hypertension.

• Journal of Agricultural Extension & Community Development
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• v.22 no.4
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• pp.385-393
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• 2015

This study is to develop evaluation indicator for brand self-diagnosis of agricultural management organizations and analyze importance weight to be used in the field. Self-diagnosis evaluation indicator of brand equity of agricultural management organizations were selected by brainstorming of brand specialists. As a result, six evaluation indicator of communication, organization, responsiveness, clarity, customer relations and quality control were selected. Importance weight of self-diagnosis evaluation indicator of brand equity of agricultural management organizations was analyzed by AHP(Analytic Hierarchy Process) and Fuzzy AHP. The results of Fuzzy AHP were as follows. Communication for 13.9%, organization for 6.5%, responsiveness for 9.9%, clarity for 7.7%, customer relations for 26.5%, and quality control for 35.5% respectively. In order to enhance brand equity of agricultural management organizations, first, cultivation guidelines should be set up to produce equal quality products among members. Second, quality levels need to be subdivided, brands and packages should be different by level. Third, persistent efforts to find new clients and distributors. Fourth, various efforts to maintain the existing excellent clients and distributors.

• Restorative Dentistry and Endodontics
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• v.48 no.2
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• pp.16.1-16.11
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• 2023

Objectives: The aim of this study was to evaluate the influence of peak kilovoltage (kVp) and a metal artifact reduction (MAR) tool on image quality and the diagnosis of vertical root fracture (VRF) in cone-beam computed tomography (CBCT). Materials and Methods: Twenty single-rooted human teeth filled with an intracanal metal post were divided into 2 groups: control (n = 10) and VRF (n = 10). Each tooth was placed into the socket of a dry mandible, and CBCT scans were acquired using a Picasso Trio varying the kVp (70, 80, 90, or 99), and the use of MAR (with or without). The examinations were assessed by 5 examiners for the diagnosis of VRF using a 5-point scale. A subjective evaluation of the expression of artifacts was done by comparing random axial images of the studied protocols. The results of the diagnoses were analyzed using 2-way analysis of variance and the Tukey post hoc test, the subjective evaluations were compared using the Friedman test, and intra-examiner reproducibility was evaluated using the weighted kappa test (α = 5%). Results: The kVp and MAR did not influence the diagnosis of VRF (p > 0.05). According to the subjective classification, the 99 kVp protocol with MAR demonstrated the least expression of artifacts, while the 70 kVp protocol without MAR led to the most artifacts. Conclusions: Protocols with higher kVp combined with MAR improved the image quality of CBCT examinations. However, those factors did not lead to an improvement in the diagnosis of VRF.

• Journal of Korean Society for Quality Management
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• v.21 no.2
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• pp.17-26
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• 1993

Process fault diagnosis is a complicated matter because quality control problems can result from a variety of causes. These causes include problems with electrical components, mechanical components, human errors, job justification errors, and air conditioning influences. In order to make the system run smoothly with minimum delay, it is necessary to suggest heuristic remedies for the detected faults. Hence, this paper describes a heuristic methodology of fault diagnosis that is performed using statistical patterns generated by quality characteristics The proposed methodology is described briefly as follows: If a sample pattern generated by random variables is similar to the number of prototype patterns, the sample pattern may be matched by any prototype pattern among them to be resembled. This concept is based on the similarity between a sample pattern and the matched prototype pattern. The similarity is calculated as the weighted average of squared deviation, which is expressed as the difference between the relative values of standard normal distribution to be transformed by the observed values of quality characteristics in a sample pattern and the critical values of the corresponding ones in a matched prototype pattern.

• Journal of Fisheries and Marine Sciences Education
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• v.22 no.4
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• pp.553-565
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• 2010

The paper aims to evaluate the quality of fisheries census statistic and to provide some desirable directions and improvements for the future fisheries census, conducted by the Government. For the quality diagnosis of fisheries census statistic, specific processes of fisheries census and statistical qualities of each dimension are surveyed and evaluated by a Government's practician, two external examiners and a research group. Results show that census design, data analysis and quality control are evaluated relatively low in specific processes, and accessibility and comparability are evaluated relatively lower than relevance, accuracy, timeliness and consistency in statistical qualities. For minimizing the sampling errors, the probability proportion method should be employed in sampling methods from currently simple sampling method. In addition, fisheries census statistic is desirable to include and compare with those of different countries for consumer oriented data system.