• Journal of the Korean Society of Safety
• /
• v.31 no.4
• /
• pp.35-41
• /
• 2016

In order to guarantee the process safety and prevent accidents, the deviations from normal operating conditions should be monitored and their root causes have to be identified as soon as possible. The statistical theories-based method among various fault diagnosis methods has been gaining popularity, due to simplicity and quickness. However, according to fault magnitudes, the scalar value generated by statistical methods can be changed and this point can lead to produce wrong information. To solve this difficulty, this work employs PCA (Principal Component Analysis) based method with qualitative information. In the case study of our previous study, the number of assumed faults is much smaller than that of process variables. In the case study of this study, the number of predefined faults is 19, while that of process variables is 6. It means that a fault diagnosis becomes more difficult and it is really hard to isolate a single fault with a small number of variables. The PCA model is constructed under normal operation data in order to get a loading vector and the data set of assumed faulty conditions is applied with PCA model. The significant changes on PC (Principal Components) axes are monitored with CUSUM (Cumulative Sum Control Chart) and recorded to make the information, which can be used to identify the types of fault.

• Journal of the Korean Society for Precision Engineering
• /
• v.31 no.11
• /
• pp.1031-1040
• /
• 2014

In this paper, a new condition diagnosis algorithm for the lens injection molding process using various features extracted from cavity pressure, nozzle pressure and screw position signals is developed with the aid of probability neural network (PNN) method. A new feature extraction method is developed for identifying five (5), seven (7) and two (2) critical features from cavity pressure, nozzle pressure and screw position signals, respectively. The node energies extracted from cavity and nozzle pressure signals are also considered based on wavelet packet decomposition (WPD). The PNN method is introduced to build the condition diagnosis model by considering the extracted features and node energies. A series of the lens injection molding experiments are conducted to validate the model, and it is demonstrated that the proposed condition diagnosis model is useful with high diagnosis accuracy.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.10a
• /
• pp.442-445
• /
• 1997

A challenging activity in the manufacturing industry is to perform in real time the continuous monitoring of the process state, the situation assessment and identification of the problem on line and diagnosis of the cause and importance of the problem if he process does not work properly. This paper describes LAT(Ladder Analysis Tool) system for fault tree generation to improving the fault diagnosis of CNC machine tools. The system consists of 4 steps which can automatically ladder analysis from ladder diagram to two diagnosis function models. The two diagnostic models based on he ladder diagram is switching function model and step switching function model. This system tries to overcome diagnosis deficiencies present machine tool.

• Journal of Korean Academy of Nursing Administration
• /
• v.9 no.4
• /
• pp.651-661
• /
• 2003

Purpose: This is the descriptive research project of which purpose is to acquire the practice, research, and educational data by establishing the database after confirming, classifying, and relating the nursing diagnosis, nursing intervention, and nursing outcome of Breast cancer patients by using the Yoo Hyung-sook's(2001) related 3N database model as the tool. Method : The Nursing Data occurring on Breast cancer patients nursing process was mapped to nursing diagnosis of NANDA, nursing interventions of NIC, nursing outcomes of NOC the 3N database linkage database which is related with the nursing process that was developed by using Yoo Hyung-sook's(2001). Result : 1. The nursing diagnosis were totally 505, and 26 articles of the nursing diagnosis were applied among 149 nursing diagnosis classification systems. 2. As for the nursing intervention, 250 articles(5l.4%) of nursing intervention were applied among 486 nursing intervention classification systems. 3. Regarding the nursing outcome, 28 articles(1l.2%l of the nursing outcome were applied among 250 nursing outcome classification systems. Conclusion: The result of this research in which the relating among the nursing diagnosis, nursing intervention, and nursing outcome of Breast cancer patients by using 3N nursing database was established is thought to be applied in the research and practice as well as to be utilized in the lecture or practice of the nursing process.

• Korean Chemical Engineering Research
• /
• v.60 no.2
• /
• pp.223-228
• /
• 2022

In chemical processes, unintended faults can make serious accidents. To tackle them, proper fault diagnosis models should be designed to identify the root cause of faults. To design a fault diagnosis model, a process and its data should be analyzed. However, most previous researches in the field of fault diagnosis just handle the data set of benchmark processes simulated on commercial programs. It indicates that it is really hard to get fresh data sets on real processes. In this study, real faulty conditions of an industrial polystyrene process are tested. In this process, a runaway reaction occurred and this caused a large loss since operators were late aware of the occurrence of this accident. To design a proper fault diagnosis model, we analyzed this process and a real accident data set. At first, a mode classification model based on support vector machine (SVM) was trained and principal component analysis (PCA) model for each mode was constructed under normal operation conditions. The results show that a proposed model can quickly diagnose the occurrence of a fault and they indicate that this model is able to reduce the potential loss.

• Journal of Microbiology and Biotechnology
• /
• v.19 no.7
• /
• pp.635-646
• /
• 2009

Rapid and accurate diagnosis of diseases is very important for appropriate treatment of patients. Recent advances in molecular-level interaction and detection technologies are upgrading the clinical diagnostics by providing new ways of diagnosis, with higher speed and accuracy. In particular, DNA microarrays can be efficiently used in clinical diagnostics which span from discovery of diseaserelevant genes to diagnosis using its biomarkers. Diagnostic DNA microarrays have been used for genotyping and determination of disease-relevant genes or agents causing diseases, mutation analysis, screening of single nucleotide polymorphisms (SNPs), detection of chromosome abnormalities, and global determination of posttranslational modification. The performance of DNA-microarray-based diagnosis is continuously improving by the integration of other tools. Thus, DNA microarrays will play a central role in clinical diagnostics and will become a gold standard method for disease diagnosis. In this paper, various applications of DNA microarrays in disease diagnosis are reviewed. Special effort was made to cover the information disclosed in the patents so that recent trends and missing applications can be revealed.

• Journal of Convergence Society for SMB
• /
• v.6 no.4
• /
• pp.79-84
• /
• 2016

A novel diagnosis scheme PCADP(personalized computer aided diagnosis probability) is proposed to overcome the problems mentioned above. PCADP scheme is a personalized diagnosis method based on ontology and it makes the bio-data analysis just a 'process' in the Smart healthcare service system. In addition, we offer a semantics modeling of the smart healthcare ontology framework in order to describe smart healthcare data and service specifications as meaningful representations based on this PCADP. The PCADP scheme is a kind of statistical diagnosis method which has real-time processing, characteristics of flexible structure, easy monitoring of decision process, and continuous improvement.

• Journal of Korean Academy of Nursing
• /
• v.26 no.2
• /
• pp.281-289
• /
• 1996

Neural networks have recently attracted considerable attention in the field of classification and other areas. The purpose of this study was to demonstrate an experiment using back-propagation neural network model applied to nursing diagnosis. The network's structure has three layers ; one input layer for representing signs and symptoms and one output layer for nursing diagnosis as well as one hidden layer. The first prototype of a nursing diagnosis system for patients with stomach cancer was developed with 254 nodes for the input layer and 20 nodes for the output layer of 20 nursing diagnoses, by utilizing learning data set collected from 118 patients with stomach cancer. It showed a hitting ratio of .93 when the model was developed with 20,000 times of learning, 6 nodes of hidden layer, 0.5 of momentum and 0.5 of learning coefficient. The system was primarily designed to be an aid in the clinical reasoning process. It was intended to simplify the use of nursing diagnoses for clinical practitioners. In order to validate the developed model, a set of test data from 20 patients with stomach cancer was applied to the diagnosis system. The data for 17 patients were concurrent with the result produced from the nursing diagnosis system which shows the hitting ratio of 85％. Future research is needed to develop a system with more nursing diagnoses and an evaluation process, and to expand the system to be applicable to other groups of patients.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1995.10b
• /
• pp.92-97
• /
• 1995

In medical diagnostic process we are dealing with the preliminary diagnosis based on the interview chart. We will quantify the qualitative information of a patient by dual scaling and establish both prototypes of fuzzy diagnostic sets and the fuzzy linear regressions. Its utility is shown in the diagnosis of headache and CAFDDH.

• Database Research
• /
• v.34 no.3
• /
• pp.58-68
• /
• 2018

Distributed computing helps to efficiently store and process large data on a cluster of multiple machines. The performance of distributed computing is greatly influenced depending on the state of the servers constituting the distributed system. In this paper, we propose a self-diagnosis system that collects log data in a distributed system, detects anomalies and visualizes the results in real time. First, we divide the self-diagnosis process into five stages: collecting, delivering, analyzing, storing, and visualizing stages. Next, we design a real-time self-diagnosis system that meets the goals of real-time, scalability, and high availability. The proposed system is based on Apache Flume, Apache Kafka, and Apache Storm, which are representative real-time distributed techniques. In addition, we use simple but effective moving average and 3-sigma based anomaly detection technique to minimize the delay of log data processing during the self-diagnosis process. Through the results of this paper, we can construct a distributed real-time self-diagnosis solution that can diagnose server status in real time in a complicated distributed system.