• Corrosion Science and Technology
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• v.14 no.1
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• pp.1-11
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• 2015

Local wall thinning and integrity degradation caused by several mechanisms, such as flow accelerated corrosion (FAC), cavitation, flashing and/or liquid drop impingements, are a main concern in carbon steel piping systems of nuclear power plant in terms of safety and operability. Thinned pipe management program (TPMP) had been developed and optimized to reduce the possibility of unplanned shutdown and/or power reduction due to pipe failure caused by wall thinning in the secondary side piping system. This program also consists of several technical elements such as prediction of wear rate for each component, prioritization of components for inspection, thickness measurement, calculation of actual wear and wear rate for each component. Decision making is associated with replacement or continuous service for thinned pipe components. Establishment of long-term strategy based on diagnosis of plant condition regarding overall wall thinning is also essential part of the program. Prediction models of wall thinning caused by FAC had been established for 24 operating nuclear plants. Long term strategies to manage the thinned pipe component were prepared and applied to each unit, which was reflecting plant specific design, operation, and inspection history, so that the structural integrity of piping system can be maintained. An alternative integrity assessment criterion and a computer program for thinned piping items were developed for the first time in the world, which was directly applicable to the secondary piping system of nuclear power plant. The thinned pipe management program is applied to all domestic nuclear power plants as a standard procedure form so that it contributes to preventing an accident caused by FAC.

• 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 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.

• Steel and Composite Structures
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• v.4 no.6
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• pp.471-487
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• 2004

Modular steel scaffolds are commonly used as supporting scaffolds in building construction, and traditionally, the load carrying capacities of these scaffolds are obtained from limited full-scale tests with little rational design. Structural failure of these scaffolds occurs from time to time due to inadequate design, poor installation and over-loads on sites. In general, multi-storey modular steel scaffolds are very slender structures which exhibit significant non-linear behaviour. Hence, secondary moments due to both $P-{\delta}$ and $P-{\Delta}$ effects should be properly accounted for in the non-linear analyses. Moreover, while the structural behaviour of these scaffolds is known to be very sensitive to the types and the magnitudes of restraints provided from attached members and supports, yet it is always difficult to quantify these restraints in either test or practical conditions. The problem is further complicated due to the presence of initial geometrical imperfections in the scaffolds, including both member out-of-straightness and storey out-of-plumbness, and hence, initial geometrical imperfections should be carefully incorporated. This paper presents an extensive numerical study on three different approaches in analyzing and designing multi-storey modular steel scaffolds, namely, a) Eigenmode Imperfection Approach, b) Notional Load Approach, and c) Critical Load Approach. It should be noted that the three approaches adopt different ways to allow for the non-linear behaviour of the scaffolds in the presence of initial geometrical imperfections. Moreover, their suitability and accuracy in predicting the structural behaviour of modular steel scaffolds are discussed and compared thoroughly. The study aims to develop a simplified and yet reliable design approach for safe prediction on the load carrying capacities of multi-storey modular steel scaffolds, so that engineers can ensure safe and effective use of these scaffolds in building construction.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.6
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• pp.498-507
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• 2017

Electronic equipments comprised of high density components with various packaging types have been recently applied to a satellite. Therefore, to guarantee high reliability of electrical equipment, a design approach, which can reduce the development period and cost through an early diagnosis in potential risks of failure, should be established. In the previous research, the reliability assesment of the electronic equipments have based on Steinberg's fatigue failure theory. However, this theory was not enough for further investigation of life prediction and reliability of the electronic equipments comprised of various sizes and packaging types due to its theoretical limitations and analysis results sensitivity with regard to different modeling technic. In that case, if detailed finite element model is established, aforementioned problems can be readily solved. However, this approach might arise disadvantage of spending much time. In this paper, to establish strategy for high reliability design of electronic equipment, we performed mechanical reliability evaluation of CCB (Camera Controller Box) at qualification level based on the approach using Sherlock unlike design techniques applied to existing business.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.4881-4894
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• 2014

This study selected 83 manufacturing firms that had been delisted from the KOSDAQ market from 2009 to 2012 and the sample firms for the two-paired sampling method were compared with 83 normal firms running businesses with same items or in same industry. The 75 financial ratios for five years immediately before delisting were used for Mean Difference Analysis with those of normal firms. Fifteen variables assumed to be significant variables for five consecutive years out of the analysis were used to in the Dichotomous Classification Technique, Logistic Regression Analysis and Discriminant Analysis. As a result of those three analyses, the Logistic Regression Analysis model was found to show the greatest discrimination. This study is differentiated from previous studies as it assumed that the firm's failure proceeded slowly over long period of time and it tried to predict the firm's failure earlier using the five years' historical data immediately before failure, whereas previous studies predicted it using three years' data only. This study is also differentiated from the proceeding comparative studies by its statistically complex Multi-Variate Analysis and Dichotomous Classification Analysis, which general stakeholders can easily approach.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.555-571
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• 2023

Due to South Korea's concentrated summer rainfall, constituting 70% of the annual total, landslides frequently occur during the rainy season, necessitating accurate prediction methods to mitigate associated damage. In this study, a reduced-scale and full-scale slope was configured using weathered granite soil to find the possibility of establishing measurement management criterias through landslide reproduction. The experiment focused on matric suction, analyzing changes in ground properties and failure patterns caused by rainfall infiltration. Subsequently, an unsaturated infinite slope stability analysis was conducted. By calculating the failure time when the safety factor falls below 1 for each experiment, landslide prediction was demonstrated to be possible, approximately 17 minutes prior for the reduction-scale experiment and 6.5 hours for the full-scale experiment. These findings provide useful data for establishing Korean soil slope measurement management criteria that consider the characteristics of weathered granite soil.

• IE interfaces
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• v.24 no.2
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• pp.119-127
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• 2011

Most companies have been increasing temporary work projects to maximize the usage of their resources. They also have been developing the effective techniques for analyzing and managing the state of the projects. In order to monitor the state of a project in real-time and predict the project's future state more accurately, this paper suggests the Bayesian Network (BN) as a tool for discovering the causes of project risk and presenting the failure probability of the project. The proposed BN modeling method with consideration of the Earned Value Management (EVM) method shows how to induce the predictive and conditional probability of the risk occurrence in the future. The advantages of the suggested model are (1) that the cause of a project risk can be easily figured out via the BN, (2) that the future value of the project can be sufficiently increased by updating relevant components of the project, and (3) that more credible prediction can be made in the similar and future situation by using the data obtained in current analysis. A numerical example is also given.

• Design & Manufacturing
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• v.18 no.2
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• pp.64-73
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• 2024

The purpose of this paper is to introduce a fusion method that combines the design of experiments (DOE) and machine learning to optimize the bias of plastic products. The study focuses on the plastic motor housing used in automobiles, which is manufactured through plastic injection molding. Achieving optimal molding for the motor housing involves the optimization of various molding conditions, including injection pressure, injection time, holding pressure, mold temperature, and cooling time. Failure to optimize these conditions can lead to increased product deformation. To minimize the deformation of the motor housing, the widely used Taguchi method, which is one of the design of experiment techniques, was employed to identify the injection molding conditions that affect deformation. Machine learning was then applied to various models based on the identified molding conditions. Among the models, the Random Forest model emerged as the most effective in predicting deformation amounts. The validity of the Random Forest model was also confirmed through verification. The verification results demonstrated the excellent prediction accuracy of the trained Random Forest model. By utilizing the validated model, molding conditions that minimize deformation were determined. Implementation of these optimal molding conditions led to a reduction of approximately 5.3% in deformation compared to the conditions before optimization. It is noteworthy that all injection molding outcomes presented in this paper were obtained through robust injection molding simulations, ensuring both research objectivity and speed.

• Journal of the Korean Geotechnical Society
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• v.21 no.2
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• pp.145-150
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• 2005

It is useful to select an appropriate mathematical model to predict landslide. Through observation and analysis of real-time measured time series, a reasonable mathematic model is chosen to do prediction of landslide. Two theoretical models, such as polynomial function and growth model, are suggested for the description and analysis of measured defermation from an active landslides. These models are applied herein to describe the main characteristics of defermation process for two types of landslide, namely polynomial and growth models. The TRS (tensiof rotation and settlement) sensors are applied to adopt two models, and the data analysis of two field (Neurpjae and Buksil) resulted in good coincidence between measured data and models.