• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.1
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• pp.99-111
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• 2016

This paper presents an integrated fault diagnosis algorithm for driving motor of In-wheel independent drive electric vehicle. Especially, this paper proposes a method that integrated the high level fault diagnosis and the low level fault diagnosis in order to improve a robustness and performance of the fault diagnosis system. The high level fault diagnosis is performed using the vehicle dynamics analysis and the low level fault diagnosis is carried using the motor system analysis. The validity of the high level fault diagnosis algorithms was verified through $Carsim^{(R)}$ and MATLAB/$Simulink^{(R)}$ cosimulation and the low level fault diagnosis's validity was shown by applying it to a MATLAB/$Simulink^{(R)}$ interior permanent magnet synchronous motor control system. Finally, this paper presents a fault diagnosis strategy by combining the high level fault diagnosis and the low level fault diagnosis.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.22 no.1
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• pp.45-56
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• 2018

The authors are using Qi-diagnosis (integrated diagnosis by bio-energy) that is a method of diagnosis and treatment. We applied Qi-diagnosis to the main study to lay the foundation and framework for research and education about the Korean Medicine. The authors try to describe systemically and specifically the Qi-diagnosis that the authors are using in clinical diagnosis ane treatment so that anyone can use it. The authors have been able to grasp the flow of human bio-energy through years of training. It has had many effects by applying the Qi-diagnosis to patients. The steps of the bio-energy flow have become objective. And the authors have been applied to acupuncture, herbal medicine, moxibustion, bruising treatment and anthrax anesthesia in clinical through the Qi-diagnosis. Also, it is applied to the life management of patients. It is applied to arts such as music therapy and art therapy. The deeper the depth of the Qi-diagnosis, the greater the opportunity to utilize the Qi-diagnosis. The Qi-diagnosis is the origin of the korean medicine. It was able to make diagnosis and treatment correct and to establish clues that the medical problems would be solved through the Qi-diagnosis. In order to do so, the diagnostician must be able to feel the auricular flow of the body accurately and objectively. In addition, he must have a comprehensive understanding of the overall framework of medicine. As a result, diagnosis and treatment of the patient as well as general problems of the patient can be identified and advised, so comprehensive treatment is possible. And it is not only a specific person can do it, but it is a diagnostic method that anyone can take if they take the basic steps step by step.

• Journal of the Korean Society of Safety
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• v.19 no.1
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• pp.23-30
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• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3620-3630
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• 2022

The diagnosis of abnormalities in a nuclear power plant is essential to maintain power plant safety. When an abnormal event occurs, the operator diagnoses the event and selects the appropriate abnormal operating procedures and sub-procedures to implement the necessary measures. To support this, abnormality diagnosis systems using data-driven methods such as artificial neural networks and convolutional neural networks have been developed. However, data-driven models cannot always guarantee an accurate diagnosis because they cannot simulate all possible abnormal events. Therefore, abnormality diagnosis systems should be able to detect their own potential misdiagnosis. This paper proposes a rulebased diagnostic validation algorithm using a previously developed two-stage diagnosis model in abnormal situations. We analyzed the diagnostic results of the sub-procedure stage when the first diagnostic results were inaccurate and derived a rule to filter the inconsistent sub-procedure diagnostic results, which may be inaccurate diagnoses. In a case study, two abnormality diagnosis models were built using gated recurrent units and long short-term memory cells, and consistency checks on the diagnostic results from both models were performed to detect any inconsistencies. Based on this, a re-diagnosis was performed to select the label of the second-best value in the first diagnosis, after which the diagnosis accuracy increased. That is, the model proposed in this study made it possible to detect diagnostic failures by the developed consistency check of the sub-procedure diagnostic results. The consistency check process has the advantage that the operator can review the results and increase the diagnosis success rate by performing additional re-diagnoses. The developed model is expected to have increased applicability as an operator support system in terms of selecting the appropriate AOPs and sub-procedures with re-diagnosis, thereby further increasing abnormal event diagnostic accuracy.

• The Mathematical Education
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• v.38 no.2
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• pp.145-157
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• 1999

Ability diagnosis is similar to medical diagnosis from a number of perspectives. However, a medical diagnosis is carried out by a direct observation through medical apparatuses, while an ability diagnosis is made by an indirect observation in the from of testing. In this respect, ability diagnosis is more difficult than medical diagnosis. Confined to middle school 1st year Mathematics, we collected survey data in 1996 from monthly tests. The data consist of student responses to diagnosis results on their abilities and of the effects of catch-up guidances for individual students which are provided based on their ability diagnosis outcomes. We analyzed the data and summarized the result in the paper. One of the main results is that the ability diagnosis as used in the paper has a very positive effect on catch-up study. But it is important to note that the effects vary across the ability groups, the effect appearing weaker in the lower ability group than in the higher ability group. This calls our attention to the need that the ability diagnosis and guidance for the catch-up study be differentiated among ability groups.

• Journal of Korean Academy of Nursing
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• v.19 no.1
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• pp.24-39
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• 1989

This study was conducted to investigate the present situation and problems related to the use of nursing diagnosis in practice. The data were obtained from 332 subjects (27 director of nursing service, 302 staff nurses) who worked in university hospitals in Korea from July through August 1988 using a mailed questionnaire. Data were analyzed by frequency, X$^2$ test and t-test. The findings were as follows ; 1, Clinical use of nursing diagnosis by directors of nursing service and staff nurses. 1) The majority of the nursing departments (88.9%) conducted group education on nursing diagnosis during the last 5 years and 81.5% of them kept a record format for nursing diagnosis : 88.9% of them had had prior experience with the nursing diagnosis. 2) Most of nurses (97.0%) had received education on nursing diagnosis. 2. Factors related to the clinical use of nursing diagnosis in nursing service departments and by staff nurses. 1) The one factor related to the use of nursing diagnosis in the nursing service department was the existence of a record. 2) Factors related to the use of nursing diagnosis by the staff nurses were the organization style of the nursing service department, group education during the last 5 years, existence of a record, the attitude of the director of nursing service, and prior experience of the use of the nursing diagnosis as characteristics of nursing service department and educational experience of nursing diagnosis as a character of nurse. 3. Problems with the use of nursing diagnosis. 1) The primary problem was the lack of time and personnel (mean : 3.757) ; the second problem was the lack of knowledge and will to use nursing diagnosis in practice by the staff nurses(mean : 3.546). 2) There was no significant difference in problems expressed by the director of nursing services and the nurses. The majority of nurses who worked in the university hospitals expressed interest in and concern about the use of nursing diagnosis. Most of the nurses had had education about on nursing diagnosis but use in practice was limited. The primary problem was lack of time and manpower. Strategies for improving use of the nursing diagnosis in practice : 1) Strengthening the education about nursing diagnosis and a holistic approach to understanding human beings. 2) Develop protocols for the use of nursing diagnosis. 3) Eliminate the language barrier regarding nursing diagnosis by translation into in Korean. 4) Decentralization of the nursing service to promote accountability by individual nurses for use of nursing diagnosis.

• International Journal of Safety
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• v.6 no.2
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• pp.27-32
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• 2007

For last several decades with the achievement of fast economic development, the electrical fires occupies over 30 percent of total fire incidents almost every year in Korea and not decreased in spite of much times and efforts. Electrical fire cause diagnostics are to confirm a cause for the fire by examination of fire scene. Cause diagnosis methods haven't been systematized yet, because of limits for available information, investigator's biased knowledge, etc. Therefore, in order to assist the investigators and to find out the exact causes of electrical fires, required is research for an electrical fire cause diagnosis system using DB, computer programming and some mathematical tools. The electrical fire cause diagnosis system has two functions of DB and electrical fire cause diagnosis. The cause diagnosis is conducted by a case-based reasoning on a case base and rule-based reasoning on a rule base. For the diagnosis with high reliability, a mixed reasoning approach of a case-based reasoning and fuzzy rule-based reasoning has been adopted. The electrical fire cause diagnosis system proposes the electrical fire causes inferred from the diagnosis processes, and possibility of the causes as well.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1687-1696
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• 1995

An integrated fault diagnosis system for heterogeneous manufacturing environments is developed. This system has a contrast with existing diagnosis systems in the respect that they are mostly for diagnosing faults on individual machines. In addition to the usual (e.g., audio, electrical) diagnostic signals, the characteristics of products from the machines are considered as the unifying diagnostic parameters among heterogeneous machines in the diagnosis. The system is composed of a knowledge representation scheme and a diagnostic query processing mechanism. Its knowledge representation scheme allows the diagnostic knowledges from heterogeneous unit diagnostic systems to be uniformly expressed in terms of the causal relations among relevant data items. It is flexible in the sense that causes for one relation can be effects for another may be reflected on our knowledge representation scheme. The diagnosis mechanism is based on a probabilistic inferencing method. This probablistic diagnosis mechanism provides more general diagnosis than existing ones in that it accommodates multiple causes and takes complication among causes into account. These scheme and mechanism are applied to a typical example to demonstrate how our system works.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.328-330
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• 2004

In this paper, ECG based cardiac disease diagnosis models are developed. Conventionally, ECG monitoring equipments can only measure and store ECG signals and they always require medical doctor's diagnosis actions which are not desirable for continuous ambulatory monitoring and diagnosis healthcare systems. In this paper, two kinds of neural based self cardiac disease diagnosis engines are developed and tested for four kinds of diseases, sinus bradycardia, sinus tachycardia, left bundle branch block and right bundle branch block. For diagnosis engines, error backpropagation neural network (BP) and probabilistic neural network (PNN) were applied. Five signal features including heart rate, QRS interval, PR interval, QT interval, and T wave types were selected for diagnosis characteristics. To show the validity of proposed diagnosis engine, MIT-BIH database were used to test. Test results showed that BP based diagnosis engine has 71% of diagnosis accuracy which is superior to accuracy of PNN based diagnosis engine. However, PNN based diagnosis engine showed superior diagnosis accuracy for complex-disease diagnoses than BP based diagnosis engine.

• Journal of the Korean Society of Safety
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• v.21 no.3 s.75
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• pp.16-21
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• 2006

This paper presents a knowledge-based electrical fire cause diagnosis system using the fuzzy reasoning. The cause diagnosis of electrical fires may be approached either by studying electric facilities or by investigating cause using precision instruments at the fire site. However, cause diagnosis methods for electrical fires haven't been systematized yet. The system focused on database(DB) construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. The cause diagnosis system for the electrical fire was implemented with entity-relational DB systems using Access 2000, one of DB development tools. Visual Basic is used as a DB building tool. The inference to confirm fire causes is conducted on the knowledge-based by combined approach of a case-based and a rule-based reasoning. A case-based cause diagnosis is designed to match the newly occurred fire case with the past fire cases stored in a DB by a kind of pattern recognition. The rule-based cause diagnosis includes intelligent objects having fuzzy attributes and rules, and is used for handling knowledge about cause reasoning. A rule-based using a fuzzy reasoning has been adopted. To infer the results from fire signs, a fuzzy operation of Yager sum was adopted. The reasoning is conducted on the rule-based reasoning that a rule-based DB system built with many rules derived from the existing diagnosis methods and the expertise in fire investigation. The cause diagnosis system proposes the causes obtained from the diagnosis process and showed possibility of electrical fire causes.