• 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.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.2
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• pp.35-44
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• 1998

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introduced Modular Artificial Neural Network(MANN) for this purpose. MANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trained by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing MANN with multitasking and message transfer between processes in SUN workstation. We tested MANN in reactor system.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.706-712
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• 2009

In this paper, a fault diagnosis method is proposed to detect and classifies faults that occur in press process line. An oil level automatic monitoring method is also presented to detect oil level. The FFT(fast fourier transform) frequency analysis and ART2 NN(adaptive resonance theory 2 neural network) with uneven vigilance parameters are used to achieve fault diagnosis in proposing method, and GUI(graphical user interface) program for fault diagnosis and oil level automatic monitoring using LabVIEW is produced and fault diagnosis was done. The experiment results demonstrate the effectiveness of the proposed fault diagnosis method of induction motors and oil level automatic monitor system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.790-795
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• 1994

This paper describes multiple fault diagnosis method in complex system with hierarchical structure. Complex system is divided into subsystem, item, component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. We introducd to Hierarchical Artificial Neural Network(HANN) for this purpose. HANN consists of four level neural network, first level for symptom classification, second level for item fault diagnosis, third level for component symptom classification,forth level for component fault diagnosis. Each network is multi layer perceptron with 7 inputs, 30 hidden node and 7 outputs trainined by backpropagation. UNIX IPC(Inter Process Communication) is used for implementing HANN with multitasking and message transfer between processes in SUN workstation. We tested HANN in reactor system.

• Nuclear Engineering and Technology
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• v.36 no.1
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• pp.73-82
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• 2004

The deaerator of a power plant is one of feedwater heaters in the secondary system, and it is located above the feedwater pumps. The feedwater pumps take the water from the deaerator storage tank, and the net positive suction head(NSPH) should always be ensured. To secure the sufficient NPSH, the deaerator tank is equipped with the level control system of which level sensors are critical items. And it is necessary to ascertain the sensor state on-line. For this, a model-based fault detection and diagnosis(FDD) is introduced in this study. The dynamic control model is formulated from the relation of input-output flow rates and liquid-level of the deaerator storage tank. Then an adaptive state estimator is designed for the fault detection and diagnosis of sensors. The performance and effectiveness of the proposed FDD scheme are evaluated by applying the operation data of Yonggwang Units 3 & 4.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.2
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• pp.49-58
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• 2015

Up to the present point in time, the level diagnosis system for urban reactivation have utilized various methods for establishment and management in Korea and overseas, such as city competitiveness evaluation, urban decay diagnosis, etc. However, contrary to performing diagnosis and evaluations on general cities in existing studies, it is found to be a very complex and difficult task to perform a diagnosis on the level of U-City due to its unique characteristics. It is difficult to determine the level of a U-City using a level diagnosis system used for general cities because a U-City is comprised of a connection/fusion of various structural elements. Therefore, in order to perform a systematic diagnosis of a U-City, it is necessary to primarily observe the structural characteristics of a U-city to derive a diagnosis system based on the relativity between each structural element. This study aims to propose a directivity of a U-City level diagnosis system in comprehensive consideration of various elements, such as the objective of a U-City, as well as the structural elements that compose a U-City based on the definitions prescribed in U-City legislations, including ubiquitous city planning, ubiquitous city infrastructure, ubiquitous city technology, services, etc. The results of this study are expected to provide a resolution for the regional quality differences of U-Cities, and also establish a stepping-stone for the realization of U-Cities with high degree of completion.

• Journal of information and communication convergence engineering
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• v.6 no.2
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• pp.207-212
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• 2008

There are several sensors in the liquid cargo ship. In the liquid cargo ship, we can get values from various sensors that are level sensor, temperature sensor, pressure sensor, oxygen sensor, VOCs sensor, high overfill sensor, etc. It is important to guarantee the reliability of sensors. In order to guarantee the reliability of sensors, we have to study the diagnosis of sensor fault. The technology of smart sensor is widely used. In this paper, the technology of smart sensor is applied to diagnosis of level sensor fault for liquid cargo ship. In order to diagnose sensor fault and find the sensor position, in this paper, we proposed algorithms of diagnosis of sensor fault using independent sensor diagnosis unit and self fault diagnosis using sensor modeling. Proposed methods are demonstrated by experiment and simulation. The results show that the proposed approach is useful. Proposed methods are useful to develop smart level sensor.

• Journal of Veterinary Clinics
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• v.21 no.3
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• pp.298-301
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• 2004

Pregnancy diagnosis by ultrasonography was performed for both pregnant and non-pregnant Asiatic black bears which were being under hibernation. Pregnancy was diagnosed for a pregnant bear by detecting images of heart-beat and vertebrae on ultrasonograph. Serum progesterone levels were measured for both pregnant and non-pregnant bears. The level of serum progesterone was 5.79 ng/ml for a pregnant bear and 0.76 ng/ml for a non-pregnant bear, respectively, thereby it was considered that measurement of serum progesterone level can be also useful for pregnancy diagnosis for Asiatic black bear.

• The Journal of Korean Medicine
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• v.23 no.2
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• pp.88-96
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• 2002

Objective : This study attempted to evaluate the ultrasonographic diagnosis of sinusitis compared to X-ray diagnosis and further to help the diagnosis and treatment of sinusitis through oriental medicine. Methods : Both A-mode ultrasonography and X-ray were taken of 30 symptom-positive patients and 10 symptom-negative controls. Nasal obstruction, rhinorrhea, postnasal drip, headache (facial pain), hyposmia (anosmia), throat discomfort and chronic cough were included in the symptoms of sinusitis. Both ultrasonographic and X-ray findings were divided into four groups (clear, mucosal thickening, air-fluid level and cystic shape) according to severity. Results : 1. The symptoms of patients (n=30) were as follows: nasal obstruction (83.3%), rhinorrhea (70.0%), postnasal drip (60.0%), chronic cough (53.3%), headache (40.0%), throat discomfort (40.0%), hyposmia (26.7%). 2. There was a significant correlation between symptoms and ultrasonographic findings (n=40, ${\gamma}=0.550$, P=0.001). 3. There was a significant correlation between symptoms and X-ray findings (n=40, ${\gamma}=0.555$, P=0.001). 4. There was a significant whole coincidence between ultrasonographic and X-ray findings (n=60, ${\gamma}=0.335, P=0.00l). Moreover, there was a significant coincidental trend between the two findings as they became severe (n=60, ${\gamma}=6.284$, P=0.012). 5. The distance of the ultrasonographic echoes was as follows: clear echo (n=9, from transducer pulse to air mucosa echo) $0.90{\pm}0.19cm$, mucosal thickening echo (n=23, from transducer pulse to air mucosa echo) 1.85{\pm}0.14cm, air-fluid level echo (n=26, from transducer pulse to back wall echo) $3.70{\pm}0.16cm$. 6. The highest diagnostic reliability of the ultrasonographic findings compared to X-ray findings was as follows: over-diagnosis in clear finding 77.3%, matched diagnosis in mucosal thickening finding 62.0%, matched diagnosis in air-fluid level finding 86.7%, matched diagnosis and under-diagnosis in cystic shape finding 50.0%. 7. In mucosal thickening, air-fluid level and cystic shape finding, there was a significant individual coincidence between the ultrasonographic and X-ray findings. In clear finding, there was no significant individual coincidence between the two findings. Conclusion : The ultrasonographic diagnosis significantly reflects the symptoms of sinusitis like X-ray diagnosis and is a valuable tool to screen prognostic factors such as mucosal thickening, air-fluid level and cyst. Therefore the ultrasonography will be useful for the diagnosis and treatment of sinusitis in oriental medicine.