• Journal of Periodontal and Implant Science
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• v.49 no.1
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• pp.58-58
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• 2019

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.43 no.2
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• pp.115-119
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• 2017

Maxillofacial images must be examined to find pathologies not identified during clinical examination. Unicystic ameloblastoma (UA) extending to the mandibular body and ramus was neglected on initial panoramic radiographic examination. After orthodontic therapy, a huge lesion was observed clinically and through imaging exams. After the conservative surgery, no recurrence was observed during five years of follow-up. This case emphasized the need for careful evaluation of patient images focusing on the oral diagnosis before any dental treatment planning, including orthodontic therapy.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.457-462
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• 1995

This paper describes diagnosis strategy of the Flexible Multiple Fault Diagnosis Module for forecasting faults in system and deciding current machine state form sensor information. Most studydeal with diagnosis control stategy about single fault in a system, this studies deal with multiple fault diagnosis. This strategy is consist of diagnosis control module such as backward tracking expert system shell, various neural network, numerical model to predict machine state and communication module for information exchange and cooperate between each model. This models are used to describe structure, function and behavior of subsystem, complex component and total system. Hierarchical structure is very efficient to represent structural, functional and behavioral knowledge. FT(Fault Tree). ST(Symptom Tree), FCD(Fault Consequence Diagrapy), SGM(State Graph Model) and FFM(Functional Flow Model) are used to represent hierachical structure. In this study, IA(Intelligent Agent) concept is introduced to match FT component and event symbol in diagnosed system and to transfer message between each event process. Proposed diagnosis control module is made of IPC(Inter Process Communication) method under UNIX operating system.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2070-2075
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• 2008

Identification of steady-state is the first step in developing a fault detection and diagnosis (FDD) system. In a complete FDD system, the steady-state detector will be included as a module in a self-learning algorithm which enables the working system's reference model to "tune" itself to its particular installation. In this study, a steady-state detector of a residential air conditioner based on moving windows was designed. Seven representing measurements were selected as key features for steady-state detection. The optimized moving window size and the feature thresholds was suggested through startup transient test and no-fault steady-state test. Performance of the steady-state detector was verified during indoor load change test. From the research, the general methodology to design a moving window steady-state detector was provided for vapor compression applications.

• Proceedings of the KIEE Conference
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• 1998.07a
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• pp.91-93
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• 1998

In this paper 30 kVA superconducting generator's transient-state electromagnetic analysis by FEM is described. The transient-state analysis by moving air gap technique was performed to analyze its 3 phase sudden short circuit characteristics. External circuit components were connected to generator model with end-winding resistance and inductance.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.46 no.3
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• pp.204-207
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• 2020

Benign fibro-osseous lesions occur when normal bone is replaced by cellular fibrous connective tissue and mineralized structures. One rare type of these lesions is the ossifying fibroma (OF). The aim of this study is to report an unusual case of OF in a 57-year-old female. Physical examination showed facial asymmetry without any tenderness, fluctuation, ocular pain, or ophthalmoplegia. Imaging exams revealed a solid mass involving the left maxilla and orbital floor. Surgical resection was performed without any complications or sequelae, and the histopathological results confirmed OF. Although recurrence is rare in this condition, the patient remains under follow-up.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.4
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• pp.2433-2437
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• 2013

Vascular endothelial growth factor (VEGF) is a potent regulator of angiogenesis and thereby involved in the development and progression of solid tumours. Associations between three VEGF gene polymorphisms (-634 G/C, +936 C/T, and +1612 G/A) and breast cancer risk have been extensively studied, but the currently available results are inconclusive. Our aim was to investigate associations between three VEGF gene polymorphisms and breast cancer risk in Chinese Han patients. We performed a hospital-based case-control study including 680 female incident breast cancer patients and 680 female age-matched healthy control subjects. Polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) analysis was performed to detect the three VEGF gene polymorphisms. We observed that women carriers of +936 TT genotypes [odds ratio (OR) =0.46, 95% confidence interval (CI) = 0.28, 0.76; P=0.002] or 936 T-allele (OR=0.81, 95% CI= 0.68, 0.98; P=0.03) had a protective effect concerning the disease. Our study suggested that the +1612G/A polymorphism was unlikely to be associated with breast cancer risk. The -634CC genotype was significantly associated with high tumor aggressiveness [large tumor size (OR=2.63, 95% CI=1.15, 6.02; P=0.02) and high histologic grade (OR=1.47, 95% CI= 1.06, 2.03; P=0.02)]. The genotypes were not related with other tumor characteristics such as regional or distant metastasis, stage at diagnosis, or estrogen or progesterone receptor status. Our study revealed that the VEGF -634 G/C and +936 C/T gene polymorphisms may be associated with breast cancer in Chinese Han patients.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.1
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• pp.138-145
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• 2000

This paper presents a robust fault diagnosis and fault tolerant control method for the control systems in closed-loop affected by unknown inputs or disturbances. The fault diagnostic scheme is based on the disturbance-decoupled state estimation using a 2-stage state observer for state, actuator bias and sensor bias. The estimated bias show the occurrence time, location and type of the faults directly. The estimated state is used for state feedback to achieve fault tolerant control against the faults. Simulation results show that the method has definite fault tolerant ability against actuator and sensor faults, moreover, the faults can be detected on-line, isolated and estimated simultaneously.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.9
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• pp.512-519
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• 2000

A Neural networks has been used for a expert system and fault diagnosis system. It is possible to nonlinear function mapping and parallel processing. Therefore It has been developing for a Diagnosis system of nuclear plower plant. In general Neural Networks is a static mapping but Dynamic Neural Network(DNN) is dynamic mapping.쪼두 a fault occur in system a state of system is changed with transient state. Because of a previous state signal is considered as a information DNN is better suited for diagnosis systems than static neural network. But a DNN has many weights so a real time implementation of diagnosis system is in need of a rapid network architecture. This paper presents a algorithm for RCP monitoring Alarm diagnosis system using Self Dynamic Neural Network(SDNN). SDNN has considerably fewer weights than a general DNN. Since there is no interlink among the hidden layer. The effectiveness of Alarm diagnosis system using the proposed algorithm is demonstrated by applying to RCP monitoring in Nuclear power plant.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.455-456
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• 2002

Wear is one of the main factors causing breakdown and fault of machine, so ferrography technique analyzing wear particles can be an effective way for condition monitoring and fault diagnosis. On the base of the forward multilayer neural network, a nodes self-deleting neural network model is provided in this paper. This network can itself deletes the nodes to optimize its construction. On the basis of the nodes self-deleting neural network, an intelligent ferrography diagnosis expert system (IFDES) for wear particles recognition and wear diagnosis is described. This intelligent expert system can automatically slim lip knowledge by learning from samples and realize basically the entirely automatic processing from wear particles recognition to wear diagnosis.