• Proceedings of the KSR Conference
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• 2010.06a
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• pp.2242-2248
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• 2010

Wide range of methodology of reliability prediction for system exists. For railway field, MIL-HDBK-217F, which has not been revised since early in 1990, is used for reliability prediction if historical data is not available. Since this standard has been published, quality and performance of electronic products have been improved rapidly and various kinds of items have been released, however new versions of items could not be released because the prediction standard could not follow up the speed of the production. Thus, this thesis introduces Telcordia SR-332 Issue 2 and would like to compare and analyze the result from MIL-HDBK-217F together with some cases we performed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.4
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• pp.72-80
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• 2016

The market size of plant projects in overseas is so large that domestic EPC project contractors are actively seeking the overseas projects and then trying to meet completion plans since successful fulfillment of these projects can provide great opportunities for them to expand into new foreign markets. International EPC projects involve all of the uncertainties common to domestic projects as well as uncertainties specific to foreign projects including marine transportation, customs, regulations, nationality, culture and so on. When overseas project gets off-schedule, the resulting uncertainty may trigger unexpected exceptions and then critical effects to the project performance. It usually require much more time and costs to encounter these exceptions in foreign sites compared to domestic project sites. Therefore, an exception handling approach is required to manage exceptions effectively for successful project progress in foreign project sites. In this research, we proposed a methodology for prediction and evaluation of exceptions caused by risks in international EPC projects based on sensitivity analysis and Bayesian Networks. First, we identified project schedule risks and related exceptions, which may meet during the fulfillment of foreign EPC projects that is performed in a sequence of engineering, procurement, preparatory manufacture, foreign shipping, construction, inspection and modification activities, and affect project performance, using literature review and expert interviews. The impact of exceptions to the schedule delay were also identified. Second, we proposed a methodology to predict the occurrence of exceptions caused by project risks and evaluate them. Using sensitivity analysis, we can identify activities that critically affect schedule delay and need to focus by priority. Then, we use Bayesian Networks to predict and evaluate exceptions. Third, we applied the proposed methodology to an international EPC project example to validate the proposed approach. Finally, we concluded the research with the further research topics. We expect that the proposed approach can be extended to apply in exception management in project management.

• The Journal of Society for e-Business Studies
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• v.26 no.2
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• pp.19-43
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• 2021

The prediction of box office performance in performing arts institutions is an important issue in the performing arts industry and institutions. For this, traditional prediction methodology and data mining methodology using standardized data such as cast members, performance venues, and ticket prices have been proposed. However, although it is evident that audiences tend to seek out their intentions by the performance guide poster, few attempts were made to predict box office performance by analyzing poster images. Hence, the purpose of this study is to propose a deep learning application method that can predict box office success through performance-related poster images. Prediction was performed using deep learning algorithms such as Pure CNN, VGG-16, Inception-v3, and ResNet50 using poster images published on the KOPIS as learning data set. In addition, an ensemble with traditional regression analysis methodology was also attempted. As a result, it showed high discrimination performance exceeding 85% of box office prediction accuracy. This study is the first attempt to predict box office success using image data in the performing arts field, and the method proposed in this study can be applied to the areas of poster-based advertisements such as institutional promotions and corporate product advertisements.

• 유체기계공업학회:학술대회논문집
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• 2003.12a
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• pp.549-554
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• 2003

EPRI PPM (Performance Prediction Methodology), a method used for the prediction of required thrust of valves, can not be applied to unbalanced-disk globe valves operated in the fluid when the fluid temperature is above $150^{\circ}F$ because the thrust increase caused by the friction between the valve disk and body is not considered in the PPM. In order to apply PPM to the valves, EPRI suggested new friction prediction method to be added in the code. This paper analyzes the applicability of the prediction method comparing the disk-to-body friction load predicted from the method with the measured friction load from the field tests. The maximum values from the prediction method and those obtained from the test were 268lbs and about 1500lbs, respectively. It is included that the prediction method should be improved for the realistic prediction of disk-to-body friction load.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1347-1356
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• 2024

Measuring the residual stress in the components in nuclear power plants is crucial to their safety evaluation. The instrumented indentation technique is a minimally invasive approach that can be conveniently used to determine the residual stress in structural materials in service. Because the indentation behavior of a structure with residual stresses is closely related to the elastic-plastic behavior of the indented material, an accurate understanding of the elastic-plastic behavior of the material is essential for evaluation of the residual stresses in the structures. However, due to the analytical problems associated with solving the elastic-plastic behavior, empirical equations with limited applicability have been used. In the present study, the impact of the non-equibiaxial residual stress state on indentation behavior was investigated using finite element analysis. In addition, a new nonequibiaxial residual-stress prediction methodology is proposed using a convolutional neural network, and the performance was validated. A more accurate residual-stress measurement will be possible by applying the proposed residual-stress prediction methodology in the future.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.813-818
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• 2007

Construction productivity is a key factor for efficiency evaluation of construction work process, project performance measurement, and basic data of work plan in construction industry. However, although construction productivity is important in construction industry, gathering methodology and analyzing methodology of productivity data are not well-organized therefore productivity data is not utilized in the construction industry The purpose of this study is to develop productivity prediction system using data mining technology based on activities and to suggest frameworks about productivity data collection, accumulation.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.83-93
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• 2001

Prediction of corporate failure using past financial data is well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness or neural networks in classification studies, there exists a major drawback in building and using the model. That is, the user can not readily comprehend the final rules that the neural network models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to corporate failure prediction modeling. An advantage of GAs approach offers is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.

• Journal of the Korea Concrete Institute
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• v.12 no.4
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• pp.69-78
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• 2000

Creep of concrete is the most dominating factor affecting time-dependent deformations of concrete structures. Especially, creep deformation for design and construction in prestressed concrete structures should be predicted accurately because of its close relation with the loss in prestree of prestressed concrete structures. Existing creep-prediction models for special applications contain several impractical factors such as the lack ok accuracy, the requirement of long-term test and the lack of versatility for change in material properties, ets., which should be improved. In order to improve those drawbacks, a methodology to modify the creep-prediction equation specified in current Korean concrete structures design standard (KCI-99), which underestimates creep of concrete and does not consider change of condition in mixture design, is proposed. In this study, short-term creep tests were carried out for early-age concrete within 28 days after loading and their test results on influencing factors in the equation are analysed. Then, the prediction equation was modified by using the early-age creep test results. The modified prediction equation was verified by comparing their results with results obtained from long-term creep test.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.6
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• pp.711-718
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• 2009

During the preliminary design phase of a supersonic aircraft, it is necessary to evaluate many potential engine/airframe combinations to determine the best solution to given set of mission requirements. And it is very important to establish a methodology to predict precisely afterbody drag so that accurate engine installed performance can be estimated. It was carried out in this paper to establish a methodology to predict afterbody drag of F-15K supersonic aircraft based on IMS(Integral Mean Slope) methodology, acquire afterbody drag data and compare its calculated data with the test data acquired from the wind tunnel test data based on 4.7% model scale. The comparison results showed good agreement between the calculated data and test data and it was found that the methodology described here to predict and test afterbody drag is acceptable.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.1
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• pp.58-67
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• 2020

A big data processing method to predict solar power generation using systems engineering approach is developed in this work. For developing analytical method, linear model (LM), support vector machine (SVN), and artificial neural network (ANN) technique are chosen. As evaluation indices, the cross-correlation and the mean square root of prediction error (RMSEP) are used. From multi-variable comparison test, it was found that ANN methodology provides the highest correlation and the lowest RMSEP.