• Journal of Korean Society of Steel Construction
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• v.14 no.3
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• pp.443-452
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• 2002

Construction of highway bridges is almost complete in many countries. Thus, the government and highway agencies are shifting their remaining life span from the viewpoint of system reliability. As such, it is necessary to develop maintenance models based on system reliability concept. In this paper, preventive and essential maintenance models were developed using system reliability and lifetime distribution.

• International Journal of Reliability and Applications
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• v.1 no.1
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• pp.65-79
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• 2000

This research considers a system which has an ultimate terminal event such as death, critical failure, bankruptcy together with a certain indicative events (temporary malfunction, special treatment, kind of defaults) that frequently occurs before the terminal event comes to the system. Some investigation of a model for the corresponding bivariate data of the system have been done with an explanation of the situation in terms of two continuous variables instead of continuous-discrete variables and some other properties. Also an analysis has been carried out to evaluate the effect of intermediate observation of occurrence of indicative event so that the result can be used for a possible suggestion of an intermediate observing schedule.

• Proceedings of the Korean Reliability Society Conference
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• 2002.06a
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• pp.387-401
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• 2002

The Digital Engineered Safety Feature Actuation System (DESFAS) of nuclear power plants actuates safety systems to mitigate severe accidents occurred in nuclear power plants. The reliability of the system should be evaluated in order to meet the reliability criteria of nuclear power plants. In this work, we have calculated and evaluated the lifetime of DESFAS by using Reliability Block Diagram (RBD) and failure rates of digital control components. Surveillance test is assumed in the evaluation. The result shows that the digital control component can be used in DESFAS system.

• Journal of the Korean Society for Railway
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• v.8 no.6 s.31
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• pp.544-549
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• 2005

Nowadays, most of bridge networks are complete or close to completion. The biggest challenge railroad./highway agencies and departments of transportation face is the maintenance of these networks, keeping them safe and serviceable, with limited funds. To maintain the bridges effectively, there is an urgent need to predict their remaining life from a system reliability viewpoint. And, it is necessary to develop the maintenance models based on system reliability concept. In this paper, maintenance models are developed for preventive maintenance and essential maintenance by using system reliability and lifetime distributions. The proposed model is applied to an existing railroad bridge. The optimal maintenance strategy of this bridge is obtained in terms of services life extension and cumulative maintenance cost.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.1-7
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• 2013

This paper is a case study of reliability assessment with field warranty data of Clutch Master Cylinder (CMC) in hydraulic clutch system. We estimate lifetime distribution using field warranty data which contain much useful information for understanding reliability of the system in the real-world environments. However, the estimated parameters are far from existing reference values, which seems to be caused right censored field warranty data. To modify the parameters, we use the information of the durability test which is performed to verify that the lifetime of the item meets the required level. After that, we can observe that the modified parameters are closer to the existing reference values. This case study shows a possible idea to supplement lack of right censored field warranty data and its applicability.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.126-129
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• 2020

We introduce a lifetime assessment technique using deep learning algorithm with complex electrical parameters such as resistivity, permittivity, impedance parameters as integrated indicators for predicting the degradation of the organic molecules. The evaluation system consists of fully automated in-situ measurement system and multiple layer perceptron learning system with five hidden layers and 1011 perceptra in each layer. Prediction accuracies are calculated and compared depending on the physical feature, learning hyperparameters. 62.5% of full time-series data are used for training and its prediction accuracy is estimated as r-square value of 0.99. Remaining 37.5% of the data are used for testing with prediction accuracy of 0.95. With k-fold cross-validation, the stability to the instantaneous changes in the measured data is also improved.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.333-336
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• 2006

High Pressure Annealing System was developed to improve the characteristics of low-temperature poly-silicon thin film transistors.. (TFTs). The high-pressure steam annealing was applied to the poly-silicon film made by rapid thermal annealing method. The carrier lifetime was investigated by Microwave detection of the Photo-Conductive Decay and the increase of carrier lifetime which indicates the reduction of the defect was observed by high-pressure steam annealing of 1MPa 600C 1hour.

• Proceedings of the KIEE Conference
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• 2005.07c
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• pp.2106-2108
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• 2005

This paper describes a method of lifetime expectation for the dry-type molded transformer which is widely used in the domestic distribution system. And the result of the temperature accelerated aging test for the winding insulation and the analyzed report on the reliability are included in this study.

• Proceedings of the KIEE Conference
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• 1998.11c
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• pp.910-912
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• 1998

In this paper, we purpose automatic diagnosis in online, as the fundamental study to diagnose the partial discharge mechanism and to predict the lifetime, by introduction a neural network. In the proposed method, Ire use acoustic emission sensing system and calculate a fixed quantity statistic operator by pulse number and amplitude. Using statically operators such as the center of gravity(G) and the gradient of the discharge distribute(C), we analyzed the early stage and the middle stage. the fixed quantity statistic operators are learned by a neural network. The diagnosis of insulation degradation and a lifetime prediction by the early stage time are achieved. On the basis of revealed excellent diagnosis ability through the neural network learning for the patterns during degradation, it was proved that the neural network is appropriate for degradation diagnosis and lifetime prediction in partial discharge.

• International journal of advanced smart convergence
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• v.6 no.1
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• pp.57-67
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• 2017

In this paper, propose an algorithm to improve network lifetime by equally consuming energy of LEACH - Mobile sensor nodes. LEACH is one of energy efficient protocols. However, we did not consider the mobility of nodes. Therefore, the transmission reception success rate of the moving data is reduced. LEACH-Mobile is a protocol that has improved the drawbacks of these LEACH. However, since LEACH-Mobile has a larger number of data packets and consumes more energy than LEACH, it has a disadvantage that the lifetime of the network is short. In order to improvement these disadvantage, Based on the average of the remaining energy of the node, cluster heads are elected with a number of nodes whose energies are larger than the average of the remaining energy from the member nodes. After that, by trying to increase the lifetime of the network by equalizing the remaining energy. In to confirm whether improve the lifetime of the network, In this paper, the number of nodes and the position of all nodes are varied for each specific round, the rest energy is equalized, and the algorithm which uniformly selected the cluster head is compared with LEACH.