• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1233-1241
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• 2019

The goal of the sound-based mechanical fault diagnosis technique is to automatically find abnormal signals in the machine using acoustic emission. Conventional methods of using mathematical models have been found to be inaccurate due to the complexity of industrial mechanical systems and the existence of nonlinear factors such as noise. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose an automatic fault diagnosis method using discrete wavelet transform and power spectrum density using multi pattern recognition. First, we perform DWT-based filtering analysis for noise cancelling and effective feature extraction. Next, the power spectral density(PSD) is performed on each subband of the DWT in order to effectively extract feature vectors of sound. Finally, each PSD data is extracted with the features of the classifier using multi pattern recognition. The results show that the proposed method can not only be used effectively to detect faults as well as apply to various automatic diagnosis system based on sound.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.345-361
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• 2013

Locating and assessing the severity of damage in large or complex structures is one of the most challenging problems in the field of civil engineering. Considering that the wavelet packet transform (WPT) has the ability to clearly reflect the damage characteristics of structural response signals and the artificial neural network (ANN) is capable of learning in an unsupervised manner and of forming new classes when the structural exhibits change, this paper investigates a multi-stage structural damage diagnosis method by using the WPT and ANN based on "energy-damage" theory, in which, the wavelet packet component energies are first extracted to be damage sensitive feature and then adopted as input into an improved back propagation (BP) neural network model for damage diagnosis in a step by step mode. To validate the efficacy of the presented approach of the damage diagnosis, the benchmark structure of the American Society of Civil Engineers (ASCE) is employed in the case study. The results of damage diagnosis indicate that the method herein is computationally efficient and is able to detect the existence of different damage patterns in the simulated experiment where minor, moderate and severe damages corresponds to involving in the loss of stiffness on braces or the removal bracing in various combinations.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.823-826
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• 2021

The recent COVID-19 pandemic has accentuated the need for faster and more accurate ways of diagnosing certain diseases for there to be safer and more effective early responses that help to prevent a total outbreak. In this work, we would like to approach this issue through machine learning algorithms to investigate whether or not they could serve as a viable replacement for conventional diagnosis. Through a process of training and testing various algorithms, we analyzed how successfully they can predict a patient's COVID-19 diagnosis based on a list of symptoms and also identified which algorithm is the most effective at doing so. If the necessary data, containing the symptoms and diagnoses of different cases, is provided, this method can be utilized to make a probable diagnosis of any disease besides COVID-19. This method can be used in conjunction with or in lieu of conventional diagnosis depending on the situation: if there is a lack of testing facilities or test kits, this method can be employed as it is inexhaustible and it could also be used in situations where a conventional diagnosis is proven to be inaccurate.

• Convergence Security Journal
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• v.20 no.1
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• pp.59-68
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• 2020

The purpose of this study is to consider ways to stabilize the naval unit diagnosis system that has been implemented for five years. Check the historical process and theoretical background of the naval unit diagnosis system. This is to confirm the future direction of the naval unit diagnosis system research. Therefore, the importance of this system is confirmed and the direction of development is explained through application method. In particular, the study suggested the scientific development of analytical methods, the development of analytical programs, the development of leadership diagnostic programs, the increase of personnel in the unit diagnosis team, and the acquisition of expertise and reliability. In order for the naval unit diagnosis system to develop, internal and external continuous research is required.

• Journal of Physiology & Pathology in Korean Medicine
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• v.29 no.3
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• pp.240-245
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• 2015

Pulse diagnosis, the most popular diagnostic tool in traditional Korean medicine, had had many forms but had been fixed on using wrist pulse and placing internal organs on Cun, Guan and Chi(寸 關 尺). Wang Shuhe(王叔和) suggested placing six viscera(六腑) on Cun, Guan and Chi based on relationship between external and internal meridian vessel, and Zhang Jiebin(張介賓) criticized his suggestion and insisted that pulse diagnosis should be based on the organ system. But the origin of pulse diagnosis which can be found in "(Huangdi's) Internal Classic(黃帝內經)" is a tool mainly for diagnosis of not internal organ system but meridian system. Most of material about pulse diagnosis after Ming dynasty(明代) reinterpreted pulse diagnosis in the aspect of organ system, So there has to be additional discussion about it.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.135-146
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• 1997

This paper describes fault diagnosis method in complex system with hierachical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. Fault diagnosis system can forecast faults in a system and decide from current machine state signal information. Comparing with other diagnosis system for single fault, the developed system deals with multiple fault diagnosis comprising Hierarchical Neural Network(HNN). HNN consists of four level neural network, first level for item fault symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. UNIX IPC(Inter Process Communication) is used for implementing HNN wiht multitasking and message transfer between processes in SUN workstation with X-Windows(Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural newtork operate as a separate process in HNN. The message queue take charge of information exdhange and cooperation between each neural network.

• Korean Journal of Adult Nursing
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• v.12 no.4
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• pp.507-516
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• 2000

This study examines that North American Nursing Diagnosis Association(NANDA) and Home Health Care Classification(HHCC) is appropriate to classify home health care client's nursing problems and suggests a modified nursing diagnosis classification system. Two hundred and forty-nine clients' records at a general hospital were reviewed and nursing problems were diagnosed according to each classification system. Results of this study are as follows. The major client's medical diagnosis are pregnancy, childbirth and puerperium, malignant neoplasm, and benign neoplasm. Of four hundred and sixty-three nursing problems, all nursing problems made a diagnos according to HHCC, while three hundred and eighty-five made a diagnosis according to NANDA. The HHCC diagnosis included 78 more nursing problems than NANDA. The discrepancy in the results may indicate a significant advantage to HHCC diagnosis because HHCC nomenclature was created empirically from hard data. However, this may be due to limitations in the data collection method so determination of which classification system is more useful is difficult to judge. However, nursing components of the HHCC are more concrete and clearer than human response patterns of the NANDA. Also the HHCC facilitates the documentation of patient care by computer, while using a conceptual framework consisting of 20 Care Components based on the nursing process: assessment, diagnosis, outcome identification, planning, implementation and evaluation. Accordingly, the practical application of HHCC is more useful than NANDA. Limitations of this study include a retrospective data collecting method and universality of samples. Further research for various samples that use prospective data collection method is recommended.

• The Journal of Korean Medicine
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• v.31 no.5
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• pp.41-59
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• 2010

Objective: Headache is one of the most common symptoms in primary medical care. The purpose of this study was to support medical treatment by consideration of a new CTTH (chronic tension-type headache) oriental medical diagnosis index. Methods: An Oriental medical diagnosis questionnaire was administered to a CTTH group, migraine group and normal group. The result was classified by using LDA, CART, factor diagnosis and tested in comparison with the original diagnosis. Also, weighting method based on expert opinions was done. Results: 1. The result analyzed by using LDA has an accuracy of 93.9% in comparison with the original diagnosis. 2. High accuracy showed when the test was performed with about 35 significant questions and four questions selected based on SPSS Wilks' lambda. 3. There was accuracy of 90.9% when differentiation was performed by using CART compared with original diagnosis. 4. 10 factors has a high initial value after factor analysis, consisting of questions to the similar differentiation. 5. Diagnosis formula of headache was made by using weighting method based on expert opinions. Conclusion: Oriental medical diagnosis questionnaires make it possible to classify headaches significantly. The study about weighting method of CTTH can make it possible to classify symptoms more accurately.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.11
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• pp.1009-1017
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• 2010

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying various techniques such as signal processing and pattern recognition. Recently, fault diagnosis systems using artificial neural network have been proposed. For effective fault diagnosis, this paper used MLP(multi-layer perceptron) network which is widely used in pattern classification. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes the decision method of the proper feature vectors about each fault signal for neural-network-based fault diagnosis system. We applied LPC coefficients, maximum magnitudes of each spectral section in FFT and RMS(root mean square) and variance of wavelet coefficients as feature vectors and selected appropriate feature vectors as comparing error ratios of fault diagnosis for sound, vibration and current fault signals. From experiment results, LPC coefficients and maximum magnitudes of each spectral section showed 100 % diagnosis ratios for each fault and the method using wavelet coefficients had noise-robust characteristic.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.2
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• pp.146-153
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• 2011

Model based machine condition diagnosis methods are generally using a normal and many failure models which need sufficient data to train the models. However, data, especially for failure modes of interest, is very hard to get in real applications. So their industrial applications are either severely limited or impossible when the failure models cannot be trained. In this paper, continuous hidden Markov model(CHMM) with only a normal model has been suggested as a very promising machine condition diagnosis method which can be easily used for industrial applications. Generally hidden Markov model also uses many pattern models to recognize specific patterns and the recognition results of CHMM show the likelihood trend of models. By observing this likelihood trend of a normal model, it is possible to detect failures. This method has been successively applied to arc weld defect diagnosis. The result shows CHMM's big potential as a machine condition monitoring method.