• The Korean Journal of Archival Studies
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• no.19
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• pp.251-291
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• 2009

Due to the trend toward computerization of business services in public sector and the push for e-government, the volume of records that are produced in electronic system and the types of records vary as well. Of those types, dataset is attracting everyone's attention because it is rapidly being supplied. Even though the administrative information system stipulated as an electronic record production system is increasing in number, as it is in blind spot for records management, the system can be superannuated or the records can be lost in case new system is developed. In addition, the system was designed not considering records management, it is managed in an unsatisfactory state because of not meeting the features and quality requirements as records management system. In the advanced countries, they recognized the importance of dataset and then managed the archives for dataset and carried out the project on management systems and a preservation formats for keeping data. Korea also is carrying out the researches on an dataset and individual administrative information systems, but the official scheme has not been established yet. In this study the items for managing archives which should be reflected when the administrative information system is designed was offered in two respects - an identification method and a quality requirement. The major directions for this system are as follows. First, as the dataset is a kind of an electronic record, it is necessary to reflect this factor from the design step prior to production. Second, the system should be established integrating the strategy for records management to the information strategy for the whole organization. In this study, based on such two directions the strategies to establish the identification for dataset in a frame to push e-government were suggested. The problem on the archiving steps including preservation format and the management procedures in dataset archive does not included in the research contents. In line with this, more researches on those contents as well as a variety of researches on dataset are expected to be more actively conducted.

• Journal of Internet Computing and Services
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• v.8 no.2
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• pp.105-115
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• 2007

The up-to-moment dataset is built by combining the past dataset and the recent dataset. The proposal is to compute association rules in real time. This study proposed the model, $EM_{past'}$ and algorithm that is sensitive to time. It can be utilized in real time by applying partitioned combination law after dividing the past dataset into(k-1). Also, we suggested $EM^{ES}_{past}$ applying the exponential smoothing method to $EM^p_{past'}$ When the association rules of $EM_{past'}\;EM^w_{past'\;and\;EM^{ES}_{past}$ were compared, The simulation results showed that $EM^{ES}_{past}$ is most accurate for testing dataset than $EM_{past}$ and $EM^w_{past}$ in huge dataset.

• Journal of Korean Society of Archives and Records Management
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• v.18 no.2
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• pp.109-133
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• 2018

The need for the records management of administrative information dataset has led to a broad consensus among archivists and has been continuously studied. In the meantime, information technology has greatly advanced, and the development and redevelopment of information management systems have been increasing. Nevertheless, dataset management in information management system has not been practiced in public organizations. This is because it is supposed that no practical management plan exists. From the point of view that practical dataset management methods should be based on the reality of dataset creation and management environment, this study investigates various active datasets in working administrative information systems. The examples and the information drawn from the examination are expected to contribute to dataset management planning. Moreover, the research methods can be utilized in further studies.

• Journal of the Korean Solar Energy Society
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• v.38 no.6
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• pp.27-36
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• 2018

Typical Meteorological Year Dataset is necessary for the renewable energy feasibility study. Since National Renewable Energy Laboratory has been built Typical Meteorological Year Dataset in 1978, gridded datasets taken from numerical weather prediction or satellite imagery are employed to produce Typical Meteorological Year Dataset. In general, Typical Meteorological Year Dataset is generated by using long-term in-situ observations. However, solar insolation is not usually measured at synoptic observing stations and therefore it is limited to build the Typical Meteorological Year Dataset with only in-situ observation. This study attempts to build the Typical Meteorological Year Dataset with satellite derived solar insolation as an alternative and then we evaluate the Typical Meteorological Year Dataset made by using satellite derived solar irradiance at Daejeon ground station. The solar irradiance is underestimated when satellite imagery is employed.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.1
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• pp.75-95
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• 2021

In this study, we focused on creating plans to manage the administrative information dataset of public records in closed universities. In particular, according to various reference materials and internal materials of the institution, we studied the theoretical discussion about the dataset and figured out the management status of the closed university's dataset. Therefore, as a measure for the data management of the Comprehensive Information Management System, recording targets are selected, retention periods are determined, administrative information dataset management standards are prepared, administrative information dataset evaluation and deletion are implemented, and comprehensive management systems of closed universities are established.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.193-207
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• 2021

As Deepfakes phenomenon is spreading worldwide mainly through videos in web platforms and it is urgent to address the issue on time. More recently, researchers have extensively discussed deepfake video datasets. However, it has been pointed out that the existing Deepfake datasets do not properly reflect the potential threat and realism due to various limitations. Although there is a need for research that establishes an agreed-upon concept for high-quality datasets or suggests evaluation criterion, there are still handful studies which examined it to-date. Therefore, this study focused on the development of the evaluation criterion for the Deepfake video dataset. In this study, the fitness of the Deepfake dataset was presented and evaluation criterions were derived through the review of previous studies. AHP structuralization and analysis were performed to advance the evaluation criterion. The results showed that Facial Expression, Validation, and Data Characteristics are important determinants of data quality. This is interpreted as a result that reflects the importance of minimizing defects and presenting results based on scientific methods when evaluating quality. This study has implications in that it suggests the fitness and evaluation criterion of the Deepfake dataset. Since the evaluation criterion presented in this study was derived based on the items considered in previous studies, it is thought that all evaluation criterions will be effective for quality improvement. It is also expected to be used as criteria for selecting an appropriate deefake dataset or as a reference for designing a Deepfake data benchmark. This study could not apply the presented evaluation criterion to existing Deepfake datasets. In future research, the proposed evaluation criterion will be applied to existing datasets to evaluate the strengths and weaknesses of each dataset, and to consider what implications there will be when used in Deepfake research.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.219-224
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• 2022

Purpose: This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures. Materials and Methods: Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III(Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant(Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy. Results: When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy. Conclusion: Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.33-42
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• 2022

In the existing intelligent command control system study, the analysis results of the commander's battlefield situation questions are provided from knowledge-based situation data. Analysis reporters write these results in various expressions of natural language. However, it is important to analyze situations about information and intelligence according to context. Analyzing the battlefield situation using artificial intelligence is necessary. We propose a virtual dataset generation method based on battlefield simulation scenarios in order to provide a dataset necessary for the battlefield situation analysis based on artificial intelligence. Dataset is generated after identifying battlefield knowledge elements in scenarios. When a candidate hypothesis is created, a unit hypothesis is automatically created. By combining unit hypotheses, similar identification hypothesis combinations are generated. An aggregation hypothesis is generated by grouping candidate hypotheses. Dataset generator SW implementation demonstrates that the proposed method can be generated the virtual battlefield situation dataset.

• Journal of Korean Society of Archives and Records Management
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• v.22 no.2
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• pp.27-49
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• 2022

According to the amendment of the Enforcement Decree of the Public Records Management Act in 2020, an administrative information dataset record management plan will be enacted, and the National Archives of Korea plans to establish an integrated administrative information dataset management system to support it. However, there is no specific service plan that considers the characteristics of the datasets and the Management Reference Table. Therefore, this paper compared and analyzed the current status of dataset services at 14 domestic and foreign public data portals and archives websites, derived implications, and proposed 6 service plans applicable to the integrated administrative information dataset management system. This study's results will lead to utilizing the administrative datasets and the activation of services.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.325-330
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• 2022

This paper conducts the collection and experiment of datasets for deep learning research on sign language such as sign language recognition, sign language translation, and sign language segmentation for Korean sign language. There exist difficulties for deep learning research of sign language. First, it is difficult to recognize sign languages since they contain multiple modalities including hand movements, hand directions, and facial expressions. Second, it is the absence of training data to conduct deep learning research. Currently, KETI dataset is the only known dataset for Korean sign language for deep learning. Sign language datasets for deep learning research are classified into two categories: Isolated sign language and Continuous sign language. Although several foreign sign language datasets have been collected over time. they are also insufficient for deep learning research of sign language. Therefore, we attempted to collect a large-scale Korean sign language dataset and evaluate it using a baseline model named TSPNet which has the performance of SOTA in the field of sign language translation. The collected dataset consists of a total of 11,402 image and text. Our experimental result with the baseline model using the dataset shows BLEU-4 score 3.63, which would be used as a basic performance of a baseline model for Korean sign language dataset. We hope that our experience of collecting Korean sign language dataset helps facilitate further research directions on Korean sign language.