• Economic and Environmental Geology
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• v.57 no.1
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• pp.51-71
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• 2024

Seismic exploration is one of the widely used geophysical exploration methods with various applications such as resource development, geotechnical investigation, and subsurface monitoring. It is essential for interpreting the geological characteristics of subsurface by providing accurate images of stratum structures. Typically, geological features are interpreted by visually analyzing seismic sections. However, recently, quantitative analysis of seismic data has been extensively researched to accurately extract and interpret target geological features. Seismic attribute analysis can provide quantitative information for geological interpretation based on seismic data. Therefore, it is widely used in various fields, including the analysis of oil and gas reservoirs, investigation of fault and fracture, and assessment of shallow gas distributions. However, seismic attribute analysis is sensitive to noise within the seismic data, thus additional noise attenuation is required to enhance the accuracy of the seismic attribute analysis. In this study, four kinds of seismic noise attenuation methods are applied and compared to mitigate random noise of poststack seismic data and enhance the attribute analysis results. FX deconvolution, DSMF, Noise2Noise, and DnCNN are applied to the Youngil Bay high-resolution seismic data to remove seismic random noise. Energy, sweetness, and similarity attributes are calculated from noise-removed seismic data. Subsequently, the characteristics of each noise attenuation method, noise removal results, and seismic attribute analysis results are qualitatively and quantitatively analyzed. Based on the advantages and disadvantages of each noise attenuation method and the characteristics of each seismic attribute analysis, we propose a suitable noise attenuation method to improve the result of seismic attribute analysis.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.362-365
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• 2004

This study suggests an attribute code system for earned value management system (EVMS). The attribute code system can be used for integrating various EYM data from construction schedule and cost. If the suggested attribute code is used in EVM, sorting and typical classifying of EVM data is possible in the computerized system. Those functions are an effective management tool for construction manager comparing with other EVM tools.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.1-6
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• 2019

The proposed model is developed to minimize the loss of information in incomplete data including missing data. The first step is to transform the learning data to compensate for the loss information using the data extension technique. In this conversion process, the attribute values of the data are filled with binary or probability values in one-hot encoding. Next, this conversion data is input to the deep learning model, where the number of entries is not constant depending on the cardinality of each attribute. Then, the entry values of each attribute are assigned to the respective input nodes, and learning proceeds. This is different from existing learning models, and has an unusual structure in which arbitrary attribute values are distributedly input to multiple nodes in the input layer. In order to evaluate the learning performance of the proposed model, various experiments are performed on the missing data and it shows that it is superior in terms of performance. The proposed model will be useful as an algorithm to minimize the loss in the ubiquitous environment.

• Journal of Distribution Science
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• v.16 no.8
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• pp.5-13
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• 2018

Purpose - Customer Delight is an important issue for firms and academia since delighted consumers reveal higher repurchase intentions than merely satisfied consumers and become loyal consumers. This research investigates customer delight, especially focusing on the role of surprise and attribute importance via experiment. Research design, data, and methodology - An experiment consisting of experiment, reference, and control group was performed with virtual online bookstore. For the analysis, one-way ANOVA and post-hoc analysis (LSD) were performed. Results - The experiment group that was delighted with surprise revealed the highest repurchase intention and recommendation intention among the other groups (H1 supported). Then each group was divided into attribute importance high and attribute importance low. For the group that was delighted in important attribute revealed higher repurchase and recommendation intention than the group that was delight in less important attribute (H2 supported). Conclusions - This research contributes academically for investigating the research area of customer delight and focusing on the role of surprise and attribute importance. For practical implications, this research provides information about customer delight and its several moderating variables that it is important to delight customers with surprising experience and focusing on an important attribute that consumers perceive not on a less important attribute.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.207-210
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• 2003

In this paper, we explore a new data mining capability that is based on Quantitative Attribute and Time Series. Our solution procedure consists of two steps. First, We derive an algorithm to contain the Quantitative Attribute into a set of candidate item. Second, We redefine the concepts of confidence and support for composite association rules. It is shown that proposed methode is very advantageous and can lead to prominent performance improvement.

• Fashion & Textile Research Journal
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• v.11 no.2
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• pp.252-261
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• 2009

The purpose of this study was to investigate the influence of female university students' cosmetic purchase motivation on cosmetic attribute evaluation and brand repurchase intention. Questionnaires data of 202 female university students who had purchase experience of cosmetic product in recent 6 months through off-line were analyzed. The results are as follows. First, situational purchase motivation had a positive impact on extrinsic and economic attributes. Second, intrinsic purchase motivation had a positive impact on extrinsic, utilitarian, aesthetic, and economic attributes. Third, hedonic purchase motivation had a positive impact on extrinsic attribute. Fourth, aesthetic attribute had a positive influence on brand repurchase intention and extrinsic attribute had a negative effect on brand repurchase intention. Therefore, when cosmetic companies dealing with female university students' cosmetic product establish marketing strategies, they need to pay attention to aesthetic attribute evaluation and intrinsic purchase motivation to highten their brand loyalty.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.275-284
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• 2008

The digital map needs efficiently updating. Because it is a base map at each local government and several geographic information systems and that is the key to enhancing to use spatial data. We suggest the linking method of building registers to the building layers of digital map, to update attribute data of the building layers. To conduct that, it is very important that each building in two data is linked by one-to-one matching. In this paper, we generate the strategy for renewing attribute data of the building layers based on identifier by using identifier of the new address system.

• Interdisciplinary Bio Central
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• v.3 no.3
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• pp.9.1-9.5
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• 2011

Entity Attribute Value (EAV) is the widely used solution to represent high dimensional and sparse data, but EAV is not search efficient for knowledge extraction. In this paper, we have proposed a search efficient data model: Optimized Entity Attribute Value (OEAV) for physical representation of high dimensional and sparse data as an alternative of widely used EAV. We have implemented both EAV and OEAV models in a data warehousing en-vironment and performed different relational and warehouse queries on both the models. The experimental results show that OEAV is dramatically search efficient and occupy less storage space compared to EAV.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4028-4049
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• 2014

Attribute-based encryption (ABE) is a promising cryptographic primitive for implementing fine-grained data sharing in cloud computing. However, before ABE can be widely deployed in practical cloud storage systems, a challenging issue with regard to attributes and user revocation has to be addressed. To our knowledge, most of the existing ABE schemes fail to support flexible and direct revocation owing to the burdensome update of attribute secret keys and all the ciphertexts. Aiming at tackling the challenge above, we formalize the notion of ciphertext-policy ABE supporting flexible and direct revocation (FDR-CP-ABE), and present a concrete construction. The proposed scheme supports direct attribute and user revocation. To achieve this goal, we introduce an auxiliary function to determine the ciphertexts involved in revocation events, and then only update these involved ciphertexts by adopting the technique of broadcast encryption. Furthermore, our construction is proven secure in the standard model. Theoretical analysis and experimental results indicate that FDR-CP-ABE outperforms the previous revocation-related methods.

• Journal of Information Science Theory and Practice
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• v.8 no.4
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• pp.20-28
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• 2020

In this study, the RDA DMP Common Standard (RDCS), a data model for implementing a machine actionable Data Management Plan (maDMP), was analyzed in four aspects. First, the twelve class models proposed by RDCS were analyzed. Second, whether the DMP attribute was included in the class attribute was analyzed. Third, we analyzed the namespace used for RDCS properties. Fourth, the values and identifiers used in RDCS properties were analyzed. As a result of the analysis, four directions for improvement were derived. First, it is necessary to add an academic record class to describe information such as papers and reports, which are representative academic documents. Second, the primary research institution, responsibility, resources, option attribute, and additional attributes are needed to describe the researcher's affiliation information. Third, it is necessary to additionally use a namespace such as Friend of a Friend that can be used universally. Fourth, the use of digital object identifier should be considered to identify academic literature.