• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.5
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• pp.553-560
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• 2003

On-site diagnosis of chiller performance is an essential step fur energy saving business. The main purpose of the on-site diagnosis is to predict the COP of a target chiller. Many models based on thermodynamics background have been proposed for this purpose. However, they have to be modified from chiller to chiller and require deep insight into thermodynamics that most of field engineers are often lacking in. This study focuses on developing an easy-to-use diagnostic technique that is based on adaptive neuro-fuzzy inference system (ANFIS). Quality of the training data for ANFIS, sampled over June through September, is assessed by checking COP prediction errors. The architecture of the ANFIS, its error bounds, and collection of training data are described in detail.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.2
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• pp.153-158
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• 2005

Recently, as the feasibility study shows that trans-Korea railway and trans-continental railway are advantageous, interest in high-speed railway system is increasing. Because railway vehicle is environment-friendly and safe compared with airplane and ship, its market-sharing increases gradually. KHST(Korean High Speed Train) has been developed by KRRI (Korea Railroad Research Institute) for last 6 years to satisfy the need. An electric railway system is composed of high-tech subsystems, among which main electric equipment such as transformers and converter are critical components determining the performance of rolling stock. We developed a measurement system for on-line test and evaluation of performances of KHST. The measurement system is composed of software part and hardware part. Perfect interface between multi-users is possible. A now method to measure temperature was applied to the measurement system. By using the system, fault diagnosis and performance evaluation of electric equipment in Korean High Speed Train was conducted during test running.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.354-361
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• 2004

To improve fault isolation performance of the Bayes isolator, this paper proposes the Fuzzy-Bayes isolator, which uses the Fuzzy-Bayes classifier as a fault isolator. The Fuzzy-Bayes classifier is composed of the Bayes classifier and weighting factor, which is determined by fuzzy inference logic. The Mahalanobis distance derivative is mapped to the weighting factor by fuzzy inference logic. The Fuzzy-Bayes fault isolator is designed for the BLDC motor fault diagnosis system. Fault isolation performance is evaluated by the experiments. The research results indicate that the Fuzzy-Bayes fault isolator improves fault isolation performance and that it can reduce the transition region chattering that is occurred when the fault is injected. In the experiment, chattering is reduced by about half that of the Bayes classifier's.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8B
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• pp.701-706
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• 2006

System-level diagnosis plays an important technique for fault detection in multi-processor systems. Efficient diagnosis is very important for real time systems as well as multiprocessor systems. Feng(1) proposed two adaptive diagnosis algorithms HADA and IHADA for hypercube system. The diagnosis cost, measured by diagnosis time and the number of test links, depends on the number and location of the faults. In this paper, we propose an adaptive diagnosis algorithm using the syndrome analysis. This removes unnecessary overhead generated in HADA and IHADA algorithm sand give a better performance compared to Feng's Method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3000-3005
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• 2015

Reliable monitoring and diagnosis of industrial processes is quite important for in terms of quality and safety. The goal of fault diagnosis is to find process variables responsible for causing specific abnormalities of the process. This work presents a classification-based diagnostic scheme based on nonlinear representation of process data. The use of a nonlinear kernel technique is able to reduce the size of the data considered and provides efficient and reliable representation of the measurement data. As a filtering stage a preprocessing is performed to eliminate unwanted parts of the data with enhanced performance. The case study of an industrial batch process has shown that the performance of the scheme outperformed other methods. In addition, the use of a nonlinear representation technique and filtering improved the diagnosis performance in the case study.

• Parasites, Hosts and Diseases
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• v.52 no.4
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• pp.377-381
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• 2014

Microscopy is considered as the gold standard for malaria diagnosis although its wide application is limited by the requirement of highly experienced microscopists. PCR and serological tests provide efficient diagnostic performance and have been applied for malaria diagnosis and research. The aim of this study was to investigate the diagnostic performance of nested PCR and a recently developed an ELISA-based new rapid diagnosis test (RDT), NovaLisa test kit, for diagnosis of malaria infection, using microscopic method as the gold standard. The performance of nested-PCR as a malaria diagnostic tool is excellent with respect to its high accuracy, sensitivity, specificity, and ability to discriminate Plasmodium species. The sensitivity and specificity of nested-PCR compared with the microscopic method for detection of Plasmodium falciparum, Plasmodium vivax, and P. falciparum/P. vivax mixed infection were 71.4 vs 100%, 100 vs 98.7%, and 100 vs 95.0%, respectively. The sensitivity and specificity of the ELISA-based NovaLisa test kit compared with the microscopic method for detection of Plasmodium genus were 89.0 vs 91.6%, respectively. NovaLisa test kit provided comparable diagnostic performance. Its relatively low cost, simplicity, and rapidity enables large scale field application.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1356-1362
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• 2015

The location of PV systems in distribution system has been increased as one of countermeasure for global environmental issues. As the operation efficiency of PV systems is getting decreased year by year due to the aging phenomenon and maintenance problems, the optimal algorithm for state diagnosis in PV systems is required in order to improve operation performance in PV systems. The existing output prediction algorithms considering various parameters and conditions of PV modules could have complicated calculation process and then their results may have a possibility of significant prediction error. To solve these problems, this paper proposes an optimal prediction algorithm of PV system by using least square methods of linear regression analysis. And also, this paper presents a performance evaluation algorithm in PV modules based on the proposed optimal prediction algorithm of PV system. The simulation results show that the proposed algorithm is a practical tool of the state diagnosis for performance improvement in PV systems.

• Journal of Korean Academy of Nursing
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• v.33 no.2
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• pp.210-219
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• 2003

Purpose: The purpose of this study was to identify nursing importance and the performance of nursing interventions linked to five nursing diagnoses and find out core nursing interventions to each of the five nursing diagnosis. The five nursing diagnoses were Pain, Diarrhea, Constipation, Hyperthermia, and Infection: Risk for. Method: Data was collected from nurses working in four different hospitals. Data were analyzed using mean, SD, and paired t-test to compare difference between importance and performance of each intervention. Result: In general interventions related to medication, such as Medication Administration: IV, Medication Administration: IM, Medication Administration: Oral, Medication Management were all considered highly important and performed very often regardless of nursing diagnoses. And the level of importance was higher than the performance in most of all the interventions linked to five nursing diagnoses. Only two interventions, Medication Administration and Intravenous (IV) insertion had higher level of performance than importance in the diagnoses of Pain and Diarrhea respectively. Conclusion: Using the above findings, we now know which intervention should be performed more frequently to solve nursing problems and which interventions are more critically important to nursing diagnosis. This information can be very helpful for developing nursing information system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.551-557
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• 2019

Diagnosis of abnormal faults is essential for producing high quality products. The role of real-time diagnosis is quite increasing in the batch processes of producing high value-added products such as semiconductors, pharmaceuticals, and so forth. In this study, we evaluate the effect of variable selection and future-value estimation techniques on the performance of the diagnosis system, which is based on nonlinear classification and measurement data. The diagnostic performance can be improved by selecting only the variables that are important and have high contribution for diagnosis. Thus, the diagnostic performance of several variable selection techniques is compared and evaluated. In addition, missing data of a new batch, called future observations, should be estimated because the full data of a new batch is not available before the end of the cycle. In this work the use of different estimation techniques is analyzed. A case study on the polyvinyl chloride batch process was carried out so that optimal variable selection and estimation methods were obtained: maximum 21.9% and 13.3% improvement by variable selection and maximum 25.8% and 15.2% improvement by estimation methods.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.1
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• pp.171-184
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• 2015

The purpose of this study is to develop the self-diagnosis methodology based on process for improving the effectiveness in public institutions service and to suggest it. For this goal, we would like to select the management methodology which can be easily handled from the viewpoint of working personnel in the public institutions and make it as a process. First, the strategy for the public institutions and the strategic task for practicing it are acquired using BSC (Balanced Score Card) which is now under the active research. At this time, the relative degree of significance is derived using the AHP (Analytic Hierarchy Process) which is conducted for the professionals and management to get the degree of importance in the strategic tasks. The acquired relative degree of significance and the figures related to the performance of each strategic task derived from the subsequent questionnaire are used to get the final highly efficient strategic task through IPA (Importance Performance Analysis). In this study, this process based self diagnosis methodology will be explained in detail using the case of Project A in order to verify the effectiveness of the management science technique on the self diagnosis of the public institutions.