• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.222-230
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• 2003

Once the medical knowledge-based system has been developed, it is essential to investigate the knowledge sources of the system because knowledge sources can affect the performance of the system in great deal. This paper presents the method and the results of the reliability test done on the medical knowledge-based system CLINAID. A knowledge source tested is Cardiovascular body system data used in CLINAID. The reliability test will be done by investigating structural relationships revealed by fuzzy relational method between the components of the knowledge sources of individual body systems using syndromes as its main component. These partitions are going to be compared with the syndromes elicited from the medical experts. This paper also reports the outcome of the computations using 7 implication operators performed on Cardiovascular body system data.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.2
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• pp.64-70
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results. it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.84-88
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results, it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• Journal of Auto-vehicle Safety Association
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• v.8 no.1
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• pp.26-30
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• 2016

The steel road wheel on ventilation holes was cracked in the vehicle durability test. But the component durability test by uni-axial, CFT(Cornering Fatigue Test) and RFT(Radial Fatigue Test) had been satisfied. That is, the uni-axial component test could not forecast the crack of vehicle. Therefore this study developed the bi-axial test mode to reflect a vehicle condition(to reflect both vertical and lateral force simultaneously) based on real load data which was measured in Europe and China and developed CAE simulation too. It reproduced the cracks same as vehicle's and verified by bi-axial test machine in the LBF(Fraunhofer Institute for Structural Durability and System Reliability) durability research center in Germany. Finally this the durability CAE simulation by using HMC(Hyundai Motor Company)'s the bi-axial test mode predicts feasibly the steel wheel's durability performance before vehicle durability test.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.9 no.1
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• pp.1-7
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• 2013

In around 2000, the U.S. NPPs have developed the various advanced engineering processes based on the INPO AP-913(Equipment Reliability Process Description) and showed the high performance in availability. With these benchmarking cases, the Korean NPPs have introduced the advanced engineering technology since 2005. The first step of the advanced engineering is to analyze and determine component importance for all components of a plant. This process is called Functional Importance Determination(FID). These results are basically utilized to determine the priority with limited resources in various areas. However, because the consistency of FID results is insufficient despite applying the same criteria in the existing operating NPPs, the degree of application is low. Therefore, this paper presents the improved methodology for FID interfacing system functions of Maintenance Rule Program and results of Single Point Vulnerability(SPV). This improved methodology is expected to contribute to enhance the reliability of FID data.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.984-990
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• 2013

Pick and placement systems need high speeds and reliability for the position alignment process of semiconductor packages in picking up and placing them on placement trays. Image processing is usually adopted for position aligning where finding out the most suitable method is considered most important aspect of the process. This paper proposes a method for judging the performance of different image processing algorithms based on the PCI (Process Capability Index). The PCI is an index which represents the error distribution acquired from many experimental data. The bigger the index, the more reliable the results or the lower the deviation. Two compared and candidate methods are Hough Transform and PCA (Principal Component Analysis), both of which are very suitable for oblong or rectangular type packages such as BGA's. Comparing the two approaches through a CPI with enough experimental results leads to the conclusion that the PCA is much better than the Hough Transform in not only reliability, but also processing speed.

• Journal of the Korea Society for Simulation
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• v.14 no.3
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• pp.91-100
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• 2005

Grid computing is widely applicable to various fields of industry including process control and manufacturing, military command and control, transportation management, and so on. In a viewpoint of application area, grid computing can be classified to three aspects that are computational grid, data grid and access grid. This paper focuses on computational grid which handles complex and large-scale computing problems. Computational grid is characterized by system dynamics which handles a variety of processors and jobs on continuous time. To solve problems of system complexity and reliability due to complex system dynamics, computational grid needs scheduling policies that allocate various jobs to proper processors and decide processing orders of allocated jobs. This paper proposes a service prediction-based job scheduling model and present its scheduling algorithm that is applicable for computational grid. The service prediction-based job scheduling model can minimize overall system execution time since the model predicts the next processing time of each processing component and distributes a job to a processing component with minimum processing time. This paper implements the job scheduling model on the DEVS modeling and simulation environment and evaluates its efficiency and reliability. Empirical results, which are compared to conventional scheduling policies, show the usefulness of service prediction-based job scheduling.

• Journal of the Society of Naval Architects of Korea
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• v.28 no.2
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• pp.228-240
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• 1991

Many experimental data of fatigue crack propagation show that the fatigue crack propagation process is stochastic. Therefore, the study on the crack propagation must be based on the probabilistic approach. In the present paper, fatigue crack propagation process is assumed to be a discrete Markov process and the method is developed, which can evaluate the reliability of the structural component by using Markov chain model(Unit step B-model) suggested by Bogdanoff. In this method, leak failure, plastic collapse and brittle fracture of the critical component are taken as failure modes, and the effects of initial crack distribution, periodic and non-periodic inspection on the probability of failure are considered. In this method, an equivalent load value for random loading such as wave load is used to facilitate the analysis. Finally some calculations are carried out in order to show the usefulness and the applicability of this method. And then some remarks on this method are mentioned.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.05a
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• pp.29-33
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• 2005

Grid computing is widely applicable to various fields of industry including process control and manufacturing, military command and control, transportation management, and so on. In a viewpoint of application area, grid computing can be classified to three aspects that are computational grid, data grid and access grid. This paper focuses on computational grid which handles complex and large-scale computing problems. Computational grid is characterized by system dynamics which handles a variety of processors and jobs on continuous time. To solve problems of system complexity and reliability due to complex system dynamics, computational grid needs scheduling policies that allocate various jobs to proper processors and decide processing orders of allocated jobs. This paper proposes the service prediction-based job scheduling model and present its algorithm that is applicable for computational grid. The service prediction-based job scheduling model can minimize overall system execution time since the model predicts a processing time of each processing component and distributes a job to processing component with minimum processing time. This paper implements the job scheduling model on the DEVSJAVA modeling and simulation environment and simulates with a case study to evaluate its efficiency and reliability Empirical results, which are compared to the conventional scheduling policies such as the random scheduling and the round-robin scheduling, show the usefulness of service prediction-based job scheduling.

• Journal of IKEEE
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• v.22 no.4
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• pp.1140-1146
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• 2018

Recently, a methodology for analyzing complex bio-signals using a deep learning model has emerged among studies that recognize human emotions. At this time, the accuracy of emotion classification may be changed depending on the evaluation method and reliability depending on the kind of data to be learned. In the case of biological signals, the reliability of data is determined according to the noise ratio, so that the noise detection method is as important as that. Also, according to the methodology for defining emotions, appropriate emotional evaluation methods will be needed. In this paper, we propose a wavelet -based noise threshold setting algorithm for verifying the reliability of data for multimodal bio-signal data labeled Valence and Arousal and a method for improving the emotion recognition rate by weighting the evaluation data. After extracting the wavelet component of the signal using the wavelet transform, the distortion and kurtosis of the component are obtained, the noise is detected at the threshold calculated by the hampel identifier, and the training data is selected considering the noise ratio of the original signal. In addition, weighting is applied to the overall evaluation of the emotion recognition rate using the euclidean distance from the median value of the Valence-Arousal plane when classifying emotional data. To verify the proposed algorithm, we use ASCERTAIN data set to observe the degree of emotion recognition rate improvement.