• Journal of Korean Academy of Nursing Administration
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• v.5 no.3
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• pp.547-561
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• 1999

The purpose of this study was to determine which nursing activities are performed for patients in each of the nine KDRGs and to examine common nursing activities between patients with the nine KDRGs and special nursing activities which were not common to patients with the nine KDRGs. The study will provide basic data for estimation of nursing fees. The nine KDRGs in model project are Lens procedures, tonsillectomy, &/or adenoidectomy, appendectomy &/or not complicate principal diagnosis, vaginal delivery, cesarean section, anal & stomal procedures, inguinal & femoral hernia, uterine & adneza procedure for nonmalignancy, and simple pneumonia & pleurisy. To determine the nursing activities for each of the nine KDRG, checklists of nursing activities in each nine KDRG were developed from the literature and a total of 115 records of patients 'who were diagnosed and discharged between January and April, 1999 from a tertiary medical center. Nursing activities for each of the nine KDRG were verified through two consecutive content analyses. The results of study are followed as: 1. The checklists of nursing activities developed included direct and indirect nursing activities, for a total of 241 nursing activities. Direct nursing consisted of physical, educational, emotional-socioecomomic-spiritual nursing in 17 areas. Indirect nursing had four areas. 2. Through the two consecutive content analyses, 197 nursing activities were selected, having item CVIs of .83 or more. Those included 81 nursing activities for Lens procedures, 95 for Tonsillectomy &/or Adenoidectomy. 93 in Appendectomy &/or not complicated principal diagnosis, 155 for vaginal delivery, 172 for cesarean section, 89 for anal & stomal procedures, 93 for inguinal & femoral hernia, 108 for uterine & adneza procedures for non-malignancy, and 68 for simple pneumonia & pleurisy. 3. Nursing activities for each of the nine KDRG were compared. Activities with 80% or higher commonality within the nine KDRGs consisted of 86 of 197 nursing activities for the total designated common nursing activities, 30 common nursing activities for patients in the operation group, 45 common activities for patients in the delivery Group. Special nursing activities not common within the nine KDRGs were : 3 for Lens procedures, 1 for Tonsillectomy &/or Adenoidectomy. 2 for Appendectomy &/or not complicated principal diagnosis, 27 for vaginal delivery, 21 for Cesarean section, 6 for anal & stomal procedures, 3 for inguinal & femoral hernia, 16 for uterine & adneza procedure for non-malignancy, 8 for simple pneumonia & pleurisy. In this study, nursing activities for each of the nine KDRGs verified through two consecutive content analyses are those that are performed in the hospital. And, nursing activities for each of the nine KDRGs included all nursing activities from hospital admission to discharge. So. the checklists consisted of nursing activities that allow for an estimation of nursing fees under PPS. The classification of nursing activities in the study will provide a reference for the development of a nursing activity classification.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.401-406
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine(SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel(KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Clinical and Experimental Pediatrics
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• v.50 no.12
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• pp.1173-1179
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• 2007

The diagnosis of juvenile rheumatoid arthritis (JRA) is based on patient's age at disease onset, symptom duration, gender, and clinical manifestations. JRA is of unknown origin, begins under the age of 16, and persists for a minimum of 6 weeks. JRA is categorized into three principal types, systemic, oligoarticular and polyarticular. Infection, other connective tissue diseases, malignancy, trauma, and immunodeficiency are discussed as differential diagnoses for JRA. Because of joint damage, focusing on early diagnosis and intervention, a vigorous initial therapeutic approach must be taken in patients who have poor prognostic factors. A multidisciplinary team approach is also important for the care of patients with JRA.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.46-53
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine (SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel (KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.513-518
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• 2007

In this paper, we propose a fault diagnosis scheme tor induction motor by adopting a hierarchical classifier consisting of k-Nearest Neighbors(k-NN) and Support Vector Machine(SVM). First, some motor conditions are classified by a simple k-NN classifier in advance. And then, more complicated classes are distinguished by SVM. To obtain the normal and fault data, we established an experimental unit with induction motor system and data acquisition module. Feature extraction is performed by Principal Component Analysis(PCA). To show its effectiveness, the proposed fault diagnostic system has been intensively tested with various data acquired under the different electrical and mechanical faults with varying load.

• Current Optics and Photonics
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• v.3 no.2
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• pp.150-153
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• 2019

Larynx cancer is a potentially terminal and severe type of neck and head cancer in which malignant cells start to grow and spread upwards in the larynx, or voice box. Smoking tobacco, drinking hot beverages and drinking alcohol are the main risk factors for these tumors. In this study, we aimed to develop a precise, accurate and rapid chemometrics assisted Raman spectroscopy method for diagnosis of larynx cancer in deparaffinized tissue samples. In the proposed method, samples were deparaffinized and 20 microns of each tissue were located on a coverslip. Both healthy (n = 13) and cancerous tissues (n = 13) were exposed to a Raman laser (785 nm) and excitations were recorded between wavenumbers of $50{\sim}1500cm^{-1}$. An Orthogonal Partial Least Square algorithm was applied to evaluate the Raman spectrum obtained. Sensitivity and specificity of the proposed method is high enough with the aid of Principal Component Analysis (PCA) to test the whole model. Healthy and cancerous tissues were accurately and precisely clustered. A rapid, easy and precise diagnosis algorithm was developed for larynx cancer. By this method, some useful data about differences in biomolecules of each group (phospholipids, amides, tyrosine, phenylalanine collagen etc.) was also obtained from the spectra. It is claimed that the optimized method has a great potential for clustering and separating tumor tissues from healthy ones. This novel, rapid, precise and objective diagnosis method may be an alternative for the conventional methods in literature for diagnosis of larynx cancer.

• Journal of KSNVE
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• v.5 no.4
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• pp.543-553
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• 1995

Modern steam turbine generators are being built as a higher power and larger system, experiencing more frequent starts and stops of operation due to a constant change of power demands. Hence, they are inevitably more vulnerable to various vibrations, and more often exposed to the danger of sudden vibration accidents than ever before. Even under the circumstances, in order to secure the system reliability of steampower plants and there by to supply safely the public electricity, it is important to prevent a sudden vibration accident in one hand and even when it happens, to raise an operating efficiency of the plants throught swift and precise treatments in the other. In this study, an interactive vibration diagnostic system has been developed to make the on-site vibration diagnosis of steam turbine generators possible and convenient, utilizing a note-book PC. For this purpose, at first the principal vibration phenomena, such as various unbalance and unstable vibrations as well as rubbing, misalignment, and shaft crack vibrations, have been systematically classified as grouped parameters of vibration frequencies, amplitudes, phases, rotating speeds at the time of accident, and operating conditions or condition changes. A new complex vibration diagnostic table has been constructed from the causal relations between the characteristic parameters and the principal vibration phenomena. Then, the diagnostic system has been developed to screen and issue the corresponding vibration phenomena by assigning to each user-selected combination of characteristic parameters a unique characteristic vector and comparing this vector with a diagnostic vector of each vibration phenomenon based on the constructed diagnostic table. Moreover, the diagnostic system has a logic whose diagnosis may be performed successfully by inputing only some of the corresponding characteristic parameters without having to input all the parameters. The developed diagnostic system has been applied to perform the diagnosis of several real cases of steam turbine vibration accidents. And the results have been quite satisfactory.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.276-280
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• 2016

Due to the increasing interest in safety and consistent product quality over a past few decades, demand for effective quality monitoring and safe operation in the modern industry has propelled research into statistical based fault detection and diagnosis methods. This paper describes the application of Hotelling $T^2$ index based Principal Component Analysis (PCA) method for fault detection and diagnosis in industrial processes. Multivariate statistical process control techniques are now widely used for performance monitoring and fault detection. Conventional methods such as PCA are suitable only for steady state processes. These conventional projection methods causes false alarms or missing data for the systems with transient values of processes. These issues significantly compromise the reliability of the monitoring systems. In this paper, a reliable method is used to overcome false alarms occur due to varying process conditions and missing data problems in transient states. This monitoring method is implemented and validated experimentally along with matlab. Experimental results proved the credibility of this fault detection method for both the steady state and transient operations.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.33 no.5
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• pp.1-11
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• 2001

In the past, abundant and clean water was available for paper mills'use. However, the growth of population and industry made water less available nowadays. Also, environmental regulation limits wastewater discharge, which affects mill operation cost. Therefore, paper mills are under pressure to use more recycled water and mill system closure. As a result, chemical and physical parameters of water are changing and new environment if being created for microorganisms in paper mill system as well. The more soluble or suspended organic materials are increased as more water is recycled and less or scarce dissolved oxygen is available, depending on the degree of recycled water usage. Microorganism flora ill paper mill system will be a1so shifted according to the environmental change of mill system. Anaerobic bacteria, including sulfate reducing bacteria (SRB), will be dominant in the system as very low or almost no oxygen available in the system. Nevertheless, it is common in domestic paper mills that employ the same and old biocides as a means of microbial control, and microbiological control is often less recognized or even neglected. The right biocide selection for increased reductive environment of mills is critical for operation and estimated loss from paper quality defects such as sheet break, holes due to microbiological cause is tremendous compared to the microbiological control cost. It is imperative to investigate and diagnosis the environmental change of mills for right control of cumbersome microorganisms. Several useful diagnosis tools, including new technology employing OFM(Optical Fouling Monitor) in situ, are illustrated.

• Journal of Power Electronics
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• v.16 no.1
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• pp.182-189
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• 2016

Fault detection is the research focus and priority in this study to ensure the high reliability of a proposed three-level inverter. Kernel principal component analysis (KPCA) has been widely used for feature extraction because of its simplicity. However, highlighting useful information that may be hidden under retained KPCs remains a problem. A weighted KPCA is proposed to overcome this shortcoming. Variable contribution plots are constructed to evaluate the importance of each KPC on the basis of sensitivity analysis theory. Then, different weighting values of KPCs are set to highlight the useful information. The weighted statistics are evaluated comprehensively by using the improved feature eigenvectors. The effectiveness of the proposed method is validated. The diagnosis results of the inverter indicate that the proposed method is superior to conventional KPCA.