• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.607-608
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• 2012

• Journal of Oral Medicine and Pain
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• v.49 no.3
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• pp.57-64
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• 2024

Purpose: The aim of this study is to analyze the final diagnosis and the pain characteristics of patients with suspected nonodontogenic toothache and to contribute to the knowledge on differential diagnosis. Methods: A retrospective analysis was conducted based on medical records from 185 patients. The following data were collected: age, sex, pain characteristics, radiographic results, initial diagnosis and treatment, and final diagnosis and treatment. The final diagnosis and the pain characteristics of the 3 most common final diagnoses were analyzed. Results: Myofascial pain (MFP) was the most prevalent diagnosed condition accounting for 37.8% of cases, followed by pulpal pain (P) at 31.4%, and trigeminal neuralgia (TN) at 18.9%. There were significant differences in age, onset of the pain, and pain intensity across the 3 groups (all p<0.01). TN group exhibited a lower frequency of spontaneous and continuous pain than the MFP and P groups (all p<0.001). The proportion of patients reporting pain alleviating and aggravating factors related to dental pain was significantly higher in the P group than in the MFP and TN groups (all p<0.001). A concordance rate of 57.0% was observed between the initial and the final diagnosis. Twenty-six patients underwent tooth extractions and 24 patients had root canal treatments. Conclusions: It is important to differentiate between dental pain and nonodontogenic toothache to avoid unnecessary dental treatments. Comprehending the pain characteristics of each condition, taking a thorough history taking, and performing diagnostic tests can help differential diagnosis.

• Korean Journal of Acupuncture
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• v.28 no.1
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• pp.23-37
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• 2011

Objectives : The pulse diagnosis is an important method in Oriental Medicine. The aim of this study is to measure the similarity of the diagnosis by a traditional method using doctor's hand for feeling of pulse and by pulse diagnosis apparatus using Hwang-Je (HJ) pulse analyser, Hui-Su (HS) pulse analyser on Chon, Kwan and Chuk. Methods : Four korean medical doctors and HJ pulse analyser, HS pulse analyser have measured the speed (遲數), the size (微細弱緩大), and the depth (浮沈) of pulse waves of 23 volunteers. First, four korean medical doctors measured pulse waves of volunteers. And then, the pulse waves of volunteers were measured by HJ pulse analyser, HS pulse analyser. This was performed on the right Chon, Kwan and Chuk. Results : The traditional method and the HJ pulse analyser method had the 60.9% matches on the values of the pulse speed condition, the HS pulse analyser method had the 78.3% matches on the values of the pulse speed condition. The traditional method and the HJ pulse analyser method had the 56.5% (Chon), 65.2% (Kwan), 78.3% (Chuk) matches on the values of the pulse size condition, the HS pulse analyser method had the 65.2% (Chon), 13.0% (Kwan), 39.1% (Chuk) matches on the values of the pulse size condition. The traditional method and the HJ pulse analyser method had the 43.5% (Chon), 26.1% (Kwan), 47.8% (Chuk) matches on the values of the pulse depth condition, the HS pulse analyser method had the 45.5% (Chon), 30.4% (Kwan), 36.8% (Chuk) matches on the values of the pulse depth condition. Conclusions : According to these results, we suggest that the pulse analyser is necessary to develope for its high similarities with the traditional pulse diagnosis.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.562-569
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• 2022

The alarm monitoring technology applied to existing operating ships manages data items such as temperature and pressure with AMS (Alarm Monitoring System) and provides an alarm to the crew should these sensing data exceed the normal level range. In addition, the maintenance of existing ships follows the Planned Maintenance System (PMS). whereby the sensing data measured from the equipment is monitored and if it surpasses the set range, maintenance is performed through an alarm, or the corresponding part is replaced in advance after being used for a certain period of time regardless of whether the target device has a malfunction or not. To secure the reliability and operational safety of ship engine operation, it is necessary to enable advanced diagnosis and prediction based on real-time condition monitoring data. To do so, comprehensive measurement of actual ship data, creation of a database, and implementation of a condition diagnosis monitoring system for condition-based predictive maintenance of auxiliary equipment and piping must take place. Furthermore, the system should enable management of auxiliary equipment and piping status information based on a responsive web, and be optimized for screen and resolution so that it can be accessed and used by various mobile devices such as smartphones as well as for viewing on a PC on board. This update cost is low, and the management method is easy. In this paper, we propose CBM (Condition Based Management) technology, for autonomous ships. This core technology is used to identify abnormal phenomena through state diagnosis and monitoring of pumps and purifiers among ship auxiliary equipment, and seawater and steam pipes among pipes. It is intended to provide performance diagnosis and failure prediction of ship auxiliary equipment and piping for convergence analysis, and to support preventive maintenance decision-making.

• Nuclear Engineering and Technology
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• v.46 no.6
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• pp.737-752
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• 2014

As condition-based maintenance (CBM) has risen as a new trend, there has been an active movement to apply information technology for effective implementation of CBM in power plants. This motivation is widespread in operations and maintenance, including monitoring, diagnosis, prognosis, and decision-making on asset management. Thermal efficiency analysis in nuclear power plants (NPPs) is a longstanding concern being updated with new methodologies in an advanced IT environment. It is also a prominent way to differentiate competitiveness in terms of operations and maintenance costs. Although thermal performance tests implemented using industrial codes and standards can provide officially trustworthy results, they are essentially resource-consuming and maybe even a hind-sighted technique rather than a foresighted one, considering their periodicity. Therefore, if more accurate performance monitoring can be achieved using advanced data analysis techniques, we can expect more optimized operations and maintenance. This paper proposes a framework and describes associated methodologies for in-situ thermal performance analysis, which differs from conventional performance monitoring. The methodologies are effective for monitoring, diagnosis, and prognosis in pursuit of CBM. Our enabling techniques cover the intelligent removal of random and systematic errors, deviation detection between a best condition and a currently measured condition, degradation diagnosis using a structured knowledge base, and prognosis for decision-making about maintenance tasks. We also discuss how our new methods can be incorporated with existing performance tests. We provide guidance and directions for developers and end-users interested in in-situ thermal performance management, particularly in NPPs with large steam turbines.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.599-605
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• 2018

Fault diagnosis depends critically on the selection of sensors monitoring crucial process variables. Boolean network (BN) is composed of nodes and directed edges, where the node state is quantized to the Boolean values of True or False and is determined by the logical functions of the network parameters and the states of other nodes with edges directed to this node. Since BN can describe the fault propagation in a sensor network, it can be applied to propose sensor selection strategy for fault diagnosis. In this article, a sufficient condition for parameter identifiability of BN is first proposed, based on which the sufficient condition for fault identifiability of a sensor network is given. Then, the fault identifiability condition induces a sensor selection strategy for sensor selection. Finally, the theoretical result is applied to the fault diagnosis-oriented sensor selection for a nuclear heating reactor plant, and both the numerical computation and simulation results verify the feasibility of the newly built BN-based sensor selection strategy.

• Journal of the Korea Safety Management & Science
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• v.6 no.3
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• pp.237-247
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• 2004

The condition of the manufacturing process in a factory should be diagnosed and maintained efficiently because any unexpected disorder in the process will be reason to decrease the efficiency of the overall system. However, if an expert experienced in this system leaves, there will be a problem for the efficient process diagnosis and maintenance, because disorder diagnosis within the process is normally dependent on the expert's experience. This paper suggests a process diagnosis using data mining based on the collected data from the coil-spring manufacturing process. The rules are generated for the relations between the attributes of the process and the output class of the product using a decision tree after selecting the effective attributes. Using the generated rules from decision tree, the condition of the current process is diagnosed and the possible maintenance actions are identified to correct any abnormal condition. Then, the appropriate maintenance action is recommended using the decision network.

• Proceedings of the KIEE Conference
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• 2001.11a
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• pp.224-226
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• 2001

In this paper, a novel high-voltage motor monitoring system (HVMMS) is proposed. This system monitors the insulation condition of the stator winding by on-line measurements of partial discharge (PD). Sensor, EMC (Epoxy-Mica Coupler) is used for PD measurement PD signals are continuously measured and digitized with a peak-hold A/D converter to build the database of the high-voltage motor's insulation condition. Also, this system can communicate with the central monitoring system via RS-485. This helps more efficient operation and maintenance of the generator.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.633-639
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• 2001

This paper proposes a knowledge-based expert system. which is assembled into hardware organized with sensor module. AID converter, USB. data acquisition PC and software composed of monitoring and diagnosis module combined with a frame-based method using Sohre's chart and a rule-based method. Vibration signals using various sensors are acquired by AID converter. transferred into PC and processed to obtain a continuous monitoring of the machine status displayed into several plots. Through combining frame-base which covers wide vibration causes with rule-base which gives relatively specified diagnosis results, high accuracy of fault diagnosis can be guaranteed and knowledge base can be easily extended by adding new causes or symptoms. Some examples using experimental data show the good feasibility of the proposed algorithm for condition monitoring and diagnosis of industrial rotating machinery.