• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.714-717
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• 1996

This paper presents a fault detection and diagnosis methodologies based on weighted symptom model and pattern matching between the coming fault propagation trend and the simulated one. At the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom model(WSM) is used to generate those candidates. The weight is determined from dynamic simulation. Using WSMs, the methodology can generate the cause candidates and rank them according to the probability. Secondly, the fault propagation trends identified from the partial or complete sequence of measurements are compared to the standard fault propagation trends stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The standard trends have been generated using dynamic simulation and stored a priori. The proposed methodology has been illustrated using two case studies and showed satisfactory diagnostic resolution.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1090-1095
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• 2012

In this paper, we propose a model-based fault diagnosis method applied to vibration data. The fault detection is performed by comparing estimated parameters with normal parameters and deciding if the observed changes can be explained satisfactorily in terms of noise or undermodelling. The key feature of this method is that it accounts for the effects of noise and model mismatch. And we aslo design a classifier for the fault isolation by applying the multiclass SVM (Support Vector Machine) to the estimated parameters. The proposed fault detection and isolation methods are applied to an engine vibration data to show a good performance. The proposed fault detection method is compared with a signal-based fault detection method through a performance analysis.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1503-1504
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• 2008

This paper presents stochastic methodology based fault diction and diagnosis algorithm for induction motor systems. First, we construct probability distribution model from healthy motors and then probability distribution for faulty motors is recursively calculated by means of the proposed probability estimation. We measure motor current with hall sensors as system state. The estimated probability is compared to the model to generate a residue signal which is utilized for fault detection and diagnosis, that is, where a fault is occurred. We carry out real-time induction motor experiment to evaluate efficiency and reliability of the proposed approach.

• Journal of The Association for Neo Medicine
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• v.1 no.1
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• pp.57-89
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• 1996

A Typification of diagnosis and treatment(DT) model must be done in order to generalize the objective stage to the result of treatment to internal disease in connection with the type of viscera and bowel symptom. We could find 108 DT models in internal disease from the combination of 18 types of viscera and bowel and 6 types of DT treatment processes. Thus, the typification of 108 models of DT can be viewed as a modeling processes of utilizing DT knowledge at each stage. We argue that objectivity in diagnosis and treatment of internal disease can be obtained practically from typification of DT model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.330-331
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• 2014

A mathematical model is suggested for diagnosis on a reciprocating pump. To the end, kinematic, thermodynamic and fluidic analyses are carried out for a simplified reciprocating pump model. The pressure inside the cylinder is expressed as a function of the rotation angle of a crank axle. The mathematical model consists of one cylinder with suction and discharge valves and an accumulator. The effect of valve leakage on the discharge angle is investigated. The discharge angle difference between normal state and leakage state increases with the leakage extent.

• The Korean Journal of Health Service Management
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• v.10 no.4
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• pp.13-23
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• 2016

Objectives : This research used an exploratory approach to identify factors affecting business strategies due to changes in the healthcare market and customer loyalty factors. Methods : The research model was formulated using antecedents divided into diagnosis quality, employee attitudes, and servicescape. Moreover, differences in the structured model were analyzed according to hospital size. The data were gathered through surveys on clients, who has received care at participating hospitals. From the 200 that were distributed, 150 questionnaires were analyzed, to facilitate analysis of the research model. Results : The effects of diagnosis quality, employee attitudes, and servicescape, on customer loyalty were mediated by trust. We also found the differences between small and large hospitals. Conclusions : Customer loyalty in small hospitals was affected by servicescape, whereas that in large hospitals was affected by diagnosis quality and employee attitudes. The research results could be used to develop strategies to improve customer loyalty.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.2
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.

• Smart Structures and Systems
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• v.31 no.1
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• pp.79-88
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• 2023

As the number of deteriorated buildings increases, the importance of safety diagnosis, maintenance, and the repair of buildings also increases. Traditionally, building condition assessments are performed by one person or one company and various inspections are needed. This entails a subjective judgment by the inspector, resulting in different assessment results, poor objectivity and a lack of reliability. Therefore, this study proposed a method to bring about accurate grading results of building conditions. The limitations of visual inspection and condition assessment processes previously conducted were identified by reviewing existing studies. Building defect data was collected using the reverse-engineered three-dimensional (3D) model. The accuracy of the results was verified by comparing them with the actual evaluation results. The results show a 50% time-saving to the same area with an accuracy of approximately 90%. Consequently, defect data with high objectivity and reliability were acquired by measuring the length, area, and width. In addition, the proposed method can improve the efficiency of the building diagnosis process.

• Journal of Advanced Marine Engineering and Technology
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• v.18 no.3
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• pp.85-93
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• 1994

A new diagnosis method is developed in this paper, in which the fuzzy set theory is introduced to diagnose the defects of rolling element bearings. The selection of membership function and the fuzzy operation model are discussed in detail here. The system is successfully used for various defects diagnosis of rolling element bearings.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.39-52
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• 2002

Research Direction The significant research direction in mechanical fault diagnosis area: Theorles and approaches for fault feature extracting and fault classification. Identification Complicated fault generating mechanism and its model Intelligent fault diagnosis system (including the expert system and network based remote diagnosis system) One of the Key Points： Fault feature extracting techniques based on (modern) signal processing(omitted)