• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.119-124
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• 2023

Deep learning has recently demonstrated conspicuous efficacy across diverse domains than traditional machine learning techniques, as the most popular approach for pattern recognition. The classification problems for tabular data, however, are remain for the area of traditional machine learning. This paper introduces a novel network module designed to tabular data into high-dimensional tensors. The module is integrated into conventional deep learning networks and subsequently applied to the classification of structured data. The proposed method undergoes training and validation on four datasets, culminating in an average accuracy of 90.22%. Notably, this performance surpasses that of the contemporary deep learning model, TabNet, by 2.55%p. The proposed approach acquires significance by virtue of its capacity to harness diverse network architectures, renowned for their superior performance in the domain of computer vision, for the analysis of tabular data.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1203-1210
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• 2020

Drought is caused by a combination of various hydrological or meteorological factor, so it is difficult to accurately assess drought event, but various drought indices have been developed to interpret them quantitatively. However, the drought indexes currently being used are calculated from the lack of a single variable, which is a problem that does not accurately determine the drought event caused by complex causes. Shortage of a single variable may not be a drought, but it is judged to be a drought. On the other hand, research on developing indices using unstructured data, which is widely used in big data analysis, is being carried out in other fields and proven to be superior. Therefore, in this study, we intend to calculate the drought index by combining unstructured data (news data) with weather and hydrologic information (rainfall and dam inflow) that are being used for the existing drought index, and to evaluate the utilization of drought interpretation through verification of the calculated drought index. The Clayton Copula function was used to calculate the joint drought index, and the parameter estimation was used by the calibration method. The analysis showed that the drought index, which combines unstructured data, properly expresses the drought period compared to the existing drought index (SPI, SDI). In addition, ROC scores were calculated higher than existing drought indices, making them more useful in drought interpretation. The joint drought index calculated in this study is considered highly useful in that it complements the analytical limits of the existing single variable drought index and provides excellent utilization of the drought index using unstructured data.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.1018-1021
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• 2020

최근 개인정보가 포함된 비정형 텍스트 문서들이 유출되거나 무분별하게 공개됨으로써 정보의 주체는 물론 기업들까지 피해를 받고 있다. 데이터를 공개 및 활용하기 위해 개인정보 검출 및 비식별화 과정이 필수적이지만 정형 데이터와는 달리 비정형 데이터의 경우 해당 과정을 자동으로 처리하는 데 한계가 있다. 이를 위해 딥러닝 모델들을 사용하여 자동화하려는 연구들이 있었지만 문장 내 단어의 모호성에 대한 고려 없이 단어 개체명 정보에만 의존하여 개인정보를 검출하는 형태로 진행되었다. 따라서 문장 내 단어들 중 식별 대상인 단어들도 비식별화 되어 데이터에 대한 유용성을 저해할 수 있다는 문제점을 남겼다. 본 논문에서는 문장의 의도 정보를 단어의 개체명 학습 과정에 부가적인 정보로 활용하는 개인정보 검출 모델과 개인정보 데이터의 유용성을 고려한 비식별화 기법을 제안한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.4259-4265
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• 2012

Digital technologies and raising problems in freeform building design and construction in Korea were examined in this paper. Three Korean building cases were researched by having interviews with experts and documentary survey for this purpose. The following problems and important points were drawn from this research. The necessity of panel optimization, significance of the secure file conversion, difficulties in securing constructability of freeform building and using of 3D data for manufacturing panels.

• Convergence Security Journal
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• v.23 no.2
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• pp.55-66
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• 2023

Recently, there is an increasing movement to increase the value of AI learning data and to secure high-quality data based on previous research on AI learning data in all areas of society. Therefore, quality management is very important in construction projects to secure high-quality data. In this paper, quality management to secure high-quality data when building AI learning data and improvement plans for each construction process are presented. In particular, more than 80% of the data quality of unstructured data built for AI learning is determined during the construction process. In this paper, we performed quality inspection of image/video data. In addition, we identified inspection procedures and problem elements that occurred in the construction phases of acquisition, data cleaning, labeling, and models, and suggested ways to secure high-quality data by solving them. Through this, it is expected that it will be an alternative to overcome the quality deviation of data for research groups and operators participating in the construction of AI learning data.

• Information Systems Review
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• v.24 no.4
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• pp.97-118
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• 2022

As generative adversarial network (GAN) based oversampling techniques have achieved impressive results in class imbalance of unstructured dataset such as image, many studies have begun to apply it to solving the problem of imbalance in structured dataset. However, these studies have failed to reflect the characteristics of structured data due to changing the data structure into an unstructured data format. In order to overcome the limitation, this study adapted CycleGAN to reflect the characteristics of structured data, and proposed hybridization of synthetic minority oversampling technique (SMOTE) and the adapted CycleGAN. In particular, this study tried to overcome the limitations of existing studies by using a one-dimensional convolutional neural network unlike previous studies that used two-dimensional convolutional neural network. Oversampling based on the method proposed have been experimented using various datasets and compared the performance of the method with existing oversampling methods such as SMOTE and adaptive synthetic sampling (ADASYN). The results indicated the proposed hybrid oversampling method showed superior performance compared to the existing methods when data have more dimensions or higher degree of imbalance. This study implied that the classification performance of oversampling structured data can be improved using the proposed hybrid oversampling method that considers the characteristic of structured data.

• The KIPS Transactions:PartC
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• v.9C no.1
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• pp.31-38
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• 2002

Communication systems, such as Switching System, are operated in the restricted conditions that the suggested events must finish in the time-constraints. Therefore, the data in the systems requires not only rapid access time, but also completion in the restricted time. Many existing data systems have been developed and used in the communication environments. But, the system construct a structural scheme and provide users with basic data services only. In recent, as the complexity of data in the communication area is rapidly increasing, it requires the data system which can represent the unstructured dataset and complete the data access in this dataset on the restricted condition. In this paper, we propose the data model which is suitable to the unstructured multi-dataset environment. The data model supports the rapid data access for unstructured dataset and enables users to easily retrieve data needed at the execution. In addition to, we define the several algorithms to clarify the structure of our model.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.904-907
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• 2013

최근 정보통신기술이 하루가 다르게 발전함에 따라 하루에도 수많은 데이터가 흘러나오는 최근의 추세이다. 정형 데이터 뿐 아니라 비정형 데이터 분석까지 진행하는 최근의 추세에 맞춰 현 빅데이터 기술 동향을 분석한다. 빅데이터 시대를 맞아 기존의 데이터웨어하우스(DW)와 발전된 데이터웨어하우스(DW) 어플라이언스에 대해 분석하고 향후 발전 전망과 방향을 제시한다.

• Korea Information Processing Society Review
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• v.30 no.2
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• pp.23-29
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• 2023

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1187-1190
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• 2015

최근에 비정형 데이터의 잠재적 가치를 유용한 데이터로써 사용하려는 경우가 많아지고 있다. 특히 트위터는 사용자의 상태나 이벤트가 잘 나타나 있어서 하나의 사용자의 이벤트로서 간주될 수 있다. 본 논문은 트위터에서 발생하는 이벤트에 주목하여, 감기라는 이벤트를 트위터 내에서 추적하고자 한다. 추적을 위해서는 트위터를 판단할 필요가 있는데, 이를 위해 기존의 감성 사전 방식 중 하나인 통계적 사전 구축을 기반으로 키워드를 활용하여 감기 판단 사전을 구축하는 방식을 제안한다.