• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.3
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• pp.143-149
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• 2018

It has become ever harder to design reliable circuits with each nanometer technology node; under normal operation conditions, a transistor device can be affected by various aging effects resulting in performance degradation and eventually design failure. The reliability (aging) effect has traditionally been the area of process engineers. However, in the future, even the smallest of variations can slow down a transistor's switching speed, and an aging device may not perform adequately at a very low voltage. Therefore, circuit designers need to consider these reliability effects in the early stages of design to make sure there are enough margins for circuits to function correctly over their entire lifetime. However, such an approach excessively increases the size and power dissipation of a system. As the impact of reliability, new techniques in designing aging-resilient circuits are necessary to reduce the impact of the aging stresses on performance, power, and yield or to predict the failure of a system. Therefore, in this paper, a novel low power on-chip self-tuning circuit considering the aging effects has been proposed.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.3
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• pp.267-275
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• 2012

In this study, crack growth in a center-cracked plate is predicted under mode I variable amplitude loading, and the result is validated by experiment. Huang's model is employed to describe crack growth with acceleration and retardation due to the variable loading effect. Experiment is conducted with Al6016-T6 plate, in which the load is applied, and crack length is measured periodically. Particle Filter algorithm, which is based on the Bayesian approach, is used to estimate model parameters from the experimental data, and predict the crack growth of the future in the probabilistic way. The prediction is validated by the run-to-failure results, from which it is observed that the method predicts well the unique behavior of crack retardation and the more data are used, the closer prediction we get to the actual run-to-failure data.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.271-274
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• 1996

In this paper we made a reliability analysis of power system pumps by using the dimensional reduction method which over comes the problem due to unavailability of enpugh data in the actual systems under many different operational environments. Hence a resonable method was proposed to determine the optimum maintenance interval of given pump in thermal power stations. This analysis was based on an actual data set of pumps for over ten years in thermal power stations belonged to Kyushu Electric Power Company, Japan.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.21-35
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• 1996

In this paper, estimation and prediction procedures are discussed for grneral situation in which the failure time follows the independent density $f_{i}({\varepsilon}_{i})$ for the accelerated life testing under Type II censoring. In the context of accelerated life test experiment, procedures are given for estimating the parameters in the Eyring model, and for estimating mean life at a given future stress level. The procedures given are conditional confidence interval procedures, obtained by conditioning on ancillary statistics. A comparison is made of these procedures and procedures based on asymptotic properties of the maximum, likelihood estimates.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.737-746
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• 2019

Most landslides in the country are shallow failures triggered by intense rainfall. Many researchers have revealed the possibility of predicting shallow failure through the volumetric water content (VWC). This study examined how to determine shallow failure using the gradient characteristics of the volumetric water content. For this, flume experiments were conducted using weathered granite soil. To confirm the saturation state of the surface layer under a rainfall intensity of 30 and 50mm/hr, VWC sensors were installed at depths of 10 and 20 cm on the upper, middle and lower slope. The test results showed that a shallow failure determination using VWC could be applied limitedly according to the slope degree. In addition, the effective cumulative rainfall due to the rainfall infiltration velocity is considered the main factor for the failure time. The failure prediction using the gradient of the VWC depends on the installation location and depth of the sensor. According to the experimental data, the measured value at 20 cm below the slope was most effective. Therefore, an analysis method of VWC and the method of selecting the installation location confirmed through this study can provide important data for presenting the measurement criteria using VWC in the future.

• Journal of the Korean GEO-environmental Society
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• v.5 no.1
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• pp.55-64
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• 2004

Stone column is a soil improvement method and can be applicable for loose sand or weak cohesive soil. Since the lack of sand in korea, stone column seems one of the most adaptable approach for poor ground as a soil improvement technique. However, this method was not studied for practical application. In this paper, the most affective design parameters for the bearing capacity of stone column were studied. The parametric study of major design factors for single stone column was carried out under the bulging and general shear failure condition, respectively. Especially, a test result of single stone column by static load was compared with the bearing capacity values of suggested formulas. The analysis result showed that the ultimate bearing capacity by the formula was much less than the measured value by the static load test. Especially, the result of the parametric study under general shear failure condition showed that the bearing capacity has big difference between each suggested formulas with the variation of the major design parameters. Therefore, the result of this study can be appliable for the future stone column project.

• The Korean Journal of Financial Management
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• v.15 no.2
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• pp.369-399
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• 1998

Most of the studies on stock price predictability using the linear model conclude that there are little possibility to predict the future price movement. But some anomalous patterns may be generated by remaining market inefficiency or regulation, market system that is facilitated to prevent the market failure. And these anomalous pattern, if exist, make them difficult to predict the stock price movement with linear model. In this study, I try to find the anomalous pattern using the ANN model. And by comparing the predictability of ANN model with the predictability of correspondent linear model, I want to show the importance of recognitions of anomalous pattern in stock price prediction. I find that ANN model could have the superior performance measured with the accuracy of prediction and investment return to correspondent linear model. This result means that there may exist the anomalous pattern that can't be recognized with linear model, and it is necessary to consider the anomalous pattern to make superior prediction performance.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.35-41
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• 2022

Heavy snow damage can be prevented in advance with an appropriate security system. To develop the security system, we developed a model that predicts snow depth after a few hours when the snow depth is observed, and utilized it to calculate a failure probability with various types of greenhouses and observed snow depth data. We compared the Markov chain model and Bayesian long short-term memory models with varying input data. Markov chain model showed the worst performance, and the models that used only past snow depth data outperformed the models that used other weather data with snow depth (temperature, humidity, wind speed). Also, the models that utilized 1-hour past data outperformed the models that utilized 3-hour data and 6-hour data. Finally, the Bayesian LSTM model that uses 1-hour snow depth data was selected to predict snow depth. We compared the selected model and the shifting method, which uses present data as future data without prediction, and the model outperformed the shifting method when predicting data after 11-24 hours.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.424-429
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• 2018

In this study, an expansion joint that is susceptible to waterhammer was tested for its vibration durability. The operation data for the hydraulic actuator was the expansion length of the expansion joint when the waterhammer occurred. In the case of the vibration durability test, the internal temperature status of the expansion joint was assumed to be a stress factor and a lifespan prediction model was assumed to follow the Arrhenius model. A test was carried out by increasing the internal temperature status at $30^{\circ}C$, $50^{\circ}C$, and $65^{\circ}C$. By a linear transformation of the lifespan data for each temperature, a constant value and activation energy coefficient was induced for the Arrhenius equation and verified by comparing the value of a lifetime prediction model with the experimental value at $85^{\circ}C$. The failure modes of the ongoing or finished test were leakage, bellows separation, and internal deformation. In the future, a composite lifespan prediction model, including two more stress factors, will be developed.

• Journal of radiological science and technology
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• v.45 no.6
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• pp.491-502
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• 2022

The purpose of this study was to retrospectively investigate the upper and lower control limits of treatment planning parameters using EBT film based delivery quality assurance (DQA) results and to analyze the results of statistical process control (SPC) in helical tomotherapy (HT). A total of 152 patients who passed or failed DQA results were retrospectively included in this study. Prostate (n = 66), rectal (n = 51), and large-field cancer patients, including lymph nodes (n = 35), were randomly selected. The absolute point dose difference (DD) and global gamma passing rate (GPR) were analyzed for all patients. Control charts were used to evaluate the upper and lower control limits (UCL and LCL) for all the assessed treatment planning parameters. Treatment planning parameters such as gantry period, leaf open time (LOT), pitch, field width, actual and planning modulation factor, treatment time, couch speed, and couch travel were analyzed to provide the optimal range using the DQA results. The classification and regression tree (CART) was used to predict the relative importance of variables in the DQA results from various treatment planning parameters. We confirmed that the proportion of patients with an LOT below 100 ms in the failure group was relatively higher than that in the passing group. SPC can detect QA failure prior to over dosimetric QA tolerance levels. The acceptable tolerance range of each planning parameter may assist in the prediction of DQA failures using the SPC tool in the future.