• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.9-14
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• 2023

The purpose of information system audit is to proactively identify and efficiently manage all risk factors that may arise during the process of constructing an information system, in order to assist in achieving the objectives of information system development. However, there is currently significant dissatisfaction with the quality and effectiveness of the auditing process, leading to ongoing research aimed at finding effective solutions. In this paper, we propose a multi-level evaluation model to enhance mutual understanding between auditors and evaluators and present a model that undergoes a maturity process, improving its levels and stages. We introduce a maturity-based evaluation model platform, enabling efficient communication between auditors and evaluators, allowing for real-time feedback, and supplementing it through continuous search. By presenting this multi-level model aimed at maturing the entire system, we aim to efficiently manage the system development process.

• The Journal of the Korea Contents Association
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• v.17 no.12
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• pp.600-616
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• 2017

This study examined the mediating effects of strengths use on the relation of strengths knowledge and career decision level among 393 college students. To analyze the data, the structural equation modeling were used. Finally, the mediating model examined the differences between goal orientation(validation-seeking orientation, growth-seeking orientation) of Dykman by carrying out of multi-group analysis. The results were as followed. First, strengths use partially mediated the relationship between strengths knowledge and career decision level. Second, the mediating model showed variation in different goal orientation type group. In other words, strengths knowledge occurs more severely and distinctively impact on career decision level in growth-seeking group than validation-seeking group. Based on the findings, implications for counseling practice, limitations of this study and recommendations for future study were discussed.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.11-19
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• 2013

This paper proposes an algorithm using vibration signals and texture analysis for mechanical fault diagnosis of an induction motor. We analyze characteristics of contrast and pattern of an image converted from vibration signal and extract three texture features using gray-level co-occurrence model(GLCM). Then, the extracted features are used as inputs of a multi-level support vector machine(MLSVM) which utilizes the radial basis function(RBF) kernel function to classify each fault type. In addition, we evaluate the classification performance with varying the parameter from 0.3 to 1.0 for the RBF kernel function of MLSVM, and the proposed algorithm achieved 100% classification accuracy with the parameter of the RBF from 0.3 to 1.0. Moreover, the proposed algorithm achieved about 98% classification accuracy with 15dB and 20dB noise inserted vibration signals.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.33-36
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• 2000

Multi-protocol Label Switching is a comparatively new technology based on the association of labels with routes and the use of labels to forward packets. MPLS can be used for transport of any level 3 protocol over any level 2 technology. Differentiated Services define a model for implementing scalable differentiation of QoS in the Internet. The inherent characteristics of MPLS make it a very good candidate for providing Differentiated Services. In this paper we describe various approaches which can be used to support differentiated services in MPLS environments.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05b
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• pp.133-136
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• 2005

Compressive strength of recycled aggregate concrete was tested by the core and by the non-destructive testing. A prediction model of compressive strength considering the replacement level of recycled aggregate was suggested by multi-regression analysis and was compared with test results. Also, Test results showed that the ratio of compressive strength by core and non-destructive testing to actual was somewhat affected by the replacement level of recycled aggregate.

• Composites Research
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• v.12 no.2
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• pp.62-69
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• 1999

During fatigue loading of composite materials, damage accumulation can be monitored by measuring their material properties. In this study, fatigue modulus is used as the damage index. Fatigue life of composite materials may be predicted analytically using damage models which are based on fatigue modulus and resultant strain. Damage models are propesed as funtions of applied stress level, number of fatigue cycle and fatigue life. The predicted life was comparable to the experimental result obtained using E-glass fiber reinforced epoxy resin materials and pultruded glass fiber reinforce polyester composites under two-stress level fatigue loading.

• Journal of the Korea Institute of Building Construction
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• v.4 no.4
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• pp.63-70
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• 2004

The objective of this study is to evaluate the compressive strength of recycled aggregate concrete by the non-destructive testing. Main experimental variables were the replacement level of recycled aggregate and blast-furnace slag, which were divided into two series according to recycled aggregate maximum size. Test results showed that a recycled aggregate had a significant influence on the non-destructive testing results, such as rebound number, Ultrasonic pulse velocity, and frequency. A prediction model of compressive strength considering the replacement level of recycled aggregate was suggested by multi-regression analysis and was compared with test results.

• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.226-235
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• 1999

The purpose of this study is to present models for evaluation and selection of Electronic Commerce systems suppliers. The Major concern of management is that almost all decision problems have multiple, usually conflicting, criteria. The fuzzified multi-objective programming models are given to accommodate the aspiration level and satisfaction level of decision makers. The proposed models are classified into three types, that is, min-operator. additive and pre-emptive priority. Numerical Examples illustrating each type of model are presented and the implications of these models are discussed.

• Journal of Ocean Engineering and Technology
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• v.13 no.2 s.32
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• pp.147-158
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• 1999

Warm or waste water discharged from offshore-based facilities often causes environmental polution as it is transported to coastal area due to tidal actions. In this research a floating-type current control structure is introduced in order to reduce the pollutant spreading in the coastal area. Effectivenss of the structure is investigated through the numerical experiment which is based on a 3-D finite difference multi-level scheme. The warm-water spreading in the bay is reduced when the draft of the structure increases and its optimum draft is found to be between 0.25h and 0.65h, where h is the water depth. The proposed structure is also tested in the Gohyun Bay and it ts proven to be applied to controllling pollutant spreading if its draft is properly chosen.

• ETRI Journal
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• v.46 no.1
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• pp.82-95
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• 2024

As automated essay scoring (AES) has progressed from handcrafted techniques to deep learning, holistic scoring capabilities have merged. However, specific trait assessment remains a challenge because of the limited depth of earlier methods in modeling dual assessments for holistic and multi-trait tasks. To overcome this challenge, we explore providing comprehensive feedback while modeling the interconnections between holistic and trait representations. We introduce the DualBERT-Trans-CNN model, which combines transformer-based representations with a novel dual-scale bidirectional encoder representations from transformers (BERT) encoding approach at the document-level. By explicitly leveraging multi-trait representations in a multi-task learning (MTL) framework, our DualBERT-Trans-CNN emphasizes the interrelation between holistic and trait-based score predictions, aiming for improved accuracy. For validation, we conducted extensive tests on the ASAP++ and TOEFL11 datasets. Against models of the same MTL setting, ours showed a 2.0% increase in its holistic score. Additionally, compared with single-task learning (STL) models, ours demonstrated a 3.6% enhancement in average multi-trait performance on the ASAP++ dataset.