• The Korean Journal of Archival Studies
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• no.3
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• pp.65-89
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• 2001

We, at The Korea Democracy Movement Archives opened temporarily at SungKongHoe University(SKHU), have currently collected about 100,000 recorded materials of democratization movement related with labor, farmer, civilian, human rights, peace, unification, young people, student and women's movements by investigating, collecting and receiving donations from civil organizations and individuals, and about 70,000 data out of this 100,000 data were converted into computer files. The Korea Democracy Movement Archives(temp) at SKHU has a significance in that it is the first archive opened by an organization. Furthermore, the opening of this Archive means the expansion of awareness on recording culture and accumulation of the achievements of the democratization movement in Korea. However, many obstacles still remain in the establishment of this Archive in a full-scale. This article examined many theoretical and realistic obstacles posed to the archivists, who are the professionals responsible for record management, in process of establishing the Archive, and the role and future perspectives of the archivists at The Korea Democracy Movement Archives(temp). The first obstacles in the process of organizing and separating the recorded materials at the Archive is a difficulty in the description of classifying the different movement organizations. The second obstacle is a difficulty in specifically applying the international standard, ISAD(G), of record description in the process of establishing the description items. Through many trials and errors, we need to try to confirm the description befitting. The Korea Democracy Movement Archives through continuous adjustment and complementary measures. The third obstacle is a difficulty in estimating the range and physical and quantitative amount of the recorded materials since the collection of recorded materials is complete. Thus, the answers to these problems lie in continuous efforts to establish a creative classification system befitting the democratization movement in Korea in the process of many trials and errors and endeavor. The evaluation classification done by archivists is a creative act forming record heritage, and archivists need to form record heritage reflecting the evaluation system of a certain period. Moreover, they transmit the shape of the current era in a maximum scale to the future by using the minimum amount of records. An archivist is responsible for two tasks, i.e., preserving a record and making other people to utilize the record by working with record. However, We, at The Korea Democracy Movement Archives(temp), have an additional task of contributing to the democratization movement in korea that has not ended by collecting, preserving and making people to utilize the fragments of memory in the recent history of Korea by establishing the Archives.

• Journal of Korean Society of Archives and Records Management
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• v.19 no.2
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• pp.147-172
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• 2019

With the provision of the original text information disclosure service, the time spent on determining the disclosure of the original text information decreased, and the number of original text information disclosure significantly increased. In public institutions, the risk of the exposure of personal information also increased. In this study, the status of personal information protection in the original text information disclosure service was investigated. Moreover, the causes of the exposure of personal information were analyzed, and improvements were proposed. The survey presented the following results. First, 13% of the original text information collected contains personal information, which is the nondisclosure information. Second, among the original text information that includes personal information, the original text information, including the personal information of the public official, was the most important. In particular, many records about vacation and medical leaves were found. Third, there were many cases in which information about the individual of the representative was exposed in the agency that deals mainly with the contract work. Fourth, a large volume of personal information was not detected by filtering personal information. Upon analyzing the cause of the exposure of personal information, the following improvements are suggested. First, privacy guidelines should be redesigned. Second, the person in charge of the task of deciding whether or not to disclose original text information should be trained further. Third, the excessive disclosure of information based on the government's quantitative performance should be eased. Fourth, the filtering function of the personal information of the original text information disclosure system should be improved.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1357-1369
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• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.

• Journal of Dental Anesthesia and Pain Medicine
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• v.21 no.4
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• pp.283-309
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• 2021

Achieving profound anesthesia in mandibular molars with irreversible pulpitis is a tedious task. This review aimed at evaluating the success of buccal/lingual infiltrations administered with a primary inferior alveolar nerve block (IANB) injection or as a supplemental injection after the failure of the primary injection in symptomatic and asymptomatic patients with irreversible pulpitis in human mandibular molars. The review question was "What will be the success of primary and supplemental infiltration injection in the endodontic treatment of patients with irreversible pulpitis in human mandibular molars?" We searched electronic databases, including Pubmed, Scopus, and Ebsco host and we did a comprehensive manual search. The review protocol was framed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist. We included clinical studies that evaluated and compared the anesthetic outcomes of primary IANB with primary and/or supplementary infiltration injections. Standard evaluation of the included studies was performed and suitable data and inferences were assessed. Twenty-six studies were included, of which 13 were selected for the meta-analysis. In the forest plot representation of the studies evaluating infiltrations, the combined risk ratio (RR) was 1.88 (95% CI: 1.49, 2.37), in favor of the secondary infiltrations with a statistical heterogeneity of 77%. The forest plot analysis for studies comparing primary IANB + infiltration versus primary IANB alone showed a low heterogeneity (0%). The included studies had similar RRs and the combined RR was 1.84 (95% CI: 1.44, 2.34). These findings suggest that supplemental infiltrations given along with a primary IANB provide a better success rate. L'Abbe plots were generated to measure the statistical heterogeneity among the studies. Trial sequential analysis suggested that the number of patients included in the analysis was adequate. Based on the qualitative and quantitative analyses, we concluded that the infiltration technique, either as a primary injection or as a supplementary injection, given after the failure of primary IANB, increases the overall anesthetic efficacy.

• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.67-75
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• 2020

Metonymy can be said to be the only language's meaning shifting technique that exists in the domain of a single human thought in order to obtain a transparent cognitive effect. The purpose of this study was to analyze the 'descriptive metonymy' of the advertising content language constructed by the cognitive principle and to find a way to use it in media language education for social and cultural interests and reflection of college students. The metonymy used in advertising media contrasts with the difficulty of the metaphorical interpretation of "opaque and distant" reasoning. Storyboards, mostly focused on human emotions and behaviors, used metonymy's 'transparent and easy meaning shifting technique'. I have found that I can expect the efficiency of media language education that contains the interest and sociocultural interest, self-reflection, and future imagination of college students. Now, there is less need to perform cognitive reasoning for advertisements with ambiguous metaphor techniques. Lastly, in order to produce successful advertising content, we expect to use the language technique of 'narrative metonymy' with warm feelings of humans, and acknowledge the lack of quantitative research and leave it as a task for the next research.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.663-668
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• 2023

Recently it is very important to collect information such as enemy positions and facilities. To this end, unmanned aerial vehicles such as multicopters have been actively developed, and various mission equipment mounted on unmanned aerial vehicles have also been developed. The coordinate-oriented algorithm refers to an algorithm that calculates a gaze angle so that the mission equipment can fix the gaze at a desired coordinate or position. Flight data and GPS data were collected and simulated using Matlab for coordinate-oriented algorithms. In the simulation using only the coordinate data, the average Pan axis angle was about 0.42°, the Tilt axis was 0.003°~0.43°, and the relatively wide error was about 0.15° on average. As a result of converting this into the distance in the NE direction, the error distance in the N direction was about 2.23m on average, and the error distance in the E direction was about -1.22m on average. The simulation applying the actual flight data showed a result of about 19m@CEP. Therefore, we conducted a study on the self-error of coordinate-oriented algorithms in monitoring and information collection, which is the main task of EOTS, and confirmed that the quantitative target of 500m is satisfied with 30m@CEP, and showed that the desired coordinates can be directed.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.217-228
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• 2023

Video instance segmentation is an intelligent visual task with high complexity because it not only requires object instance segmentation for each image frame constituting a video, but also requires accurate tracking of instances throughout the frame sequence of the video. In special, human instance segmentation in drama videos has an unique characteristic that requires accurate tracking of several main characters interacting in various places and times. Also, it is also characterized by a kind of the class imbalance problem because there is a significant difference between the frequency of main characters and that of supporting or auxiliary characters in drama videos. In this paper, we introduce a new human instance datatset called MHIS, which is built upon drama videos, Miseang, and then propose a novel video data augmentation method, CDVA, in order to overcome the data imbalance problem between character classes. Different from the previous video data augmentation methods, the proposed CDVA generates more realistic augmented videos by deciding the optimal location within the background clip for a target human instance to be inserted with taking rich spatio-temporal context embedded in videos into account. Therefore, the proposed augmentation method, CDVA, can improve the performance of a deep neural network model for video instance segmentation. Conducting both quantitative and qualitative experiments using the MHIS dataset, we prove the usefulness and effectiveness of the proposed video data augmentation method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.6
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• pp.381-389
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• 2023

One of the crucial components in building information modeling (BIM) is data. To systematically manage these data, various research studies have focused on the creation of object breakdown structures and property sets. Specifically, crucial data for managing programs and payments involves work breakdown structures (WBSs) and cost breakdown structures (CBSs), which are indispensable for mapping BIM objects. Achieving this requires disassembling CBS quantities based on 3D objects and WBS. However, this task is highly tedious owing to the large volume of CBS and divergent coding practices employed by different organizations. Manual processes, such as those based on Excel, become nearly impossible for such extensive tasks. In response to the challenge of computing quantities that are difficult to derive from BIM objects, this study presents methods for disassembling length-based quantities, incorporating significant portions of the bill of quantities (BOQs). The proposed approach recommends suitable CBS by leveraging the accumulated history of WBS-CBS mapping databases. Additionally, it establishes a unified CBS code, facilitating the effective operation of CBS databases.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.455-460
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• 2023

Text augmentation is a methodology that creates new augmented texts by transforming or generating original texts for the purpose of improving the performance of NLP models. However existing text augmentation techniques have limitations such as lack of expressive diversity semantic distortion and limited number of augmented texts. Recently text augmentation using large language models and few-shot learning can overcome these limitations but there is also a risk of noise generation due to incorrect generation. In this paper, we propose a text augmentation method called TAGS that generates multiple candidate texts and selects the appropriate text as the augmented text. TAGS generates various expressions using few-shot learning while effectively selecting suitable data even with a small amount of original text by using contrastive learning and similarity comparison. We applied this method to task-oriented chatbot data and achieved more than sixty times quantitative improvement. We also analyzed the generated texts to confirm that they produced semantically and expressively diverse texts compared to the original texts. Moreover, we trained and evaluated a classification model using the augmented texts and showed that it improved the performance by more than 0.1915, confirming that it helps to improve the actual model performance.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.505-518
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• 2023

3D point cloud semantic segmentation is a computer vision task that involves dividing the point cloud into different objects and regions by predicting the class label of each point. Existing 3D semantic segmentation models have some limitations in performing sufficient fusion of multi-modal features while ensuring both characteristics of 2D visual features extracted from RGB images and 3D geometric features extracted from point cloud. Therefore, in this paper, we propose MMCA-Net, a novel 3D semantic segmentation model using 2D-3D multi-modal features. The proposed model effectively fuses two heterogeneous 2D visual features and 3D geometric features by using an intermediate fusion strategy and a multi-modal cross attention-based fusion operation. Also, the proposed model extracts context-rich 3D geometric features from input point cloud consisting of irregularly distributed points by adopting PTv2 as 3D geometric encoder. In this paper, we conducted both quantitative and qualitative experiments with the benchmark dataset, ScanNetv2 in order to analyze the performance of the proposed model. In terms of the metric mIoU, the proposed model showed a 9.2% performance improvement over the PTv2 model using only 3D geometric features, and a 12.12% performance improvement over the MVPNet model using 2D-3D multi-modal features. As a result, we proved the effectiveness and usefulness of the proposed model.