• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.823-826
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• 2008

This paper shows linkage management for external services. There are many services for specific entities such as DBLP and OntoWorld. OntoFrame, as a Semantic Web-based research information service portal, aims at one-stop service in ways that it connects external services with hyperlinks. For managing the linkage, linkage rules are manually edited by human administrators and automatically verified and tested by linkage management system. It consists of linkage rule management, linkage rule verification, linkage test, and dynamic link generation. Linkage rule management creates and edits linkage rules to connect external services on the Web. After finished rule editing and verification step, linkage management invokes linkage test with entity list. Only valid links are visible to enable users to click on our system, and thus it increases user's reliability on the system.

• Journal of KIISE
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• v.43 no.2
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• pp.135-142
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• 2016

Visual analysis of spatiotemporal data has focused on a variety of techniques for analyzing and exploring the data. The goal of these techniques is to explore the spatiotemporal data using time information, discover patterns in the data, and analyze spatiotemporal data. The overall trend flow patterns help users analyze geo-referenced temporal events. However, it is difficult to extract and visualize overall trend flow patterns using data that has no trajectory information for movements. In order to visualize overall trend flow patterns, in this paper, we estimate continuous distributions of discrete events over time using KDE, and we extract vector fields from the continuous distributions using the gravity model. We then apply our technique on twitter data to validate techniques.

• Journal of KIISE:Databases
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• v.37 no.6
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• pp.324-332
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• 2010

The majority of event detection in real-time embedded sensor network systems is based on data fusion that uses noisy sensor data collected from complicated real-world environments. Current research has produced several excellent low-level mechanisms to collect sensor data and perform aggregation. However, solutions that enable these systems to provide real-time data processing using readings from heterogeneous sensors and subsequently detect complex events of interest in real-time fashion need further research. We are developing real-time event detection approaches which allow light-weight data fusion and do not require significant computing resources. Underlying the event detection framework is a collection of real-time monitoring and fusion mechanisms that are invoked upon the arrival of sensor data. The combination of these mechanisms and the framework has the potential to significantly improve the timeliness and reduce the resource requirements of embedded sensor networks. In addition to that, we discuss about a privacy that is foundation technique for trusted embedded sensor network system and explain anonymization technique to ensure privacy.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.1
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• pp.30-44
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• 2022

The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.

• Journal of Korean Library and Information Science Society
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• v.51 no.4
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• pp.121-156
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• 2020

Content curation aims to limit the user's experience to the most necessary content; to do this, it is necessary to identify users' information needs. Given that KISTI's Content Curation Center was established in 2018, it had little information about its users. This study intends to investigate what content scientists want to see from KISTI and how they want it curated. It investigated researchers who worked for government-funded research institutes through online surveys and telephone interviews. This study's results helped me review the current practice of KISTI content curation and give some suggestions to improve future practice. They present information on content and content curation services required by researchers; therefore, they can be used as a reference for information agencies (e.g., libraries, data centers) to improve their content curation practices.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.195-198
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• 2021

COVID-19 전염병은 우리의 일상 생활에 빠르게, 그리고 엄청난 영향을 미쳤다. 현재는 마스크를 착용하는 것이 새로운 평범함이 되었고, 이에 따라 많은 서비스 제공업체들은 고객들에게 그들의 서비스를 이용하기 위해 마스크를 착용하도록 요구하고 있다. 공공 버스도 이에 포함된다. 여러 뉴스 기사에 따르면 마스크를 써 달라는 버스 기사의 부탁에 버스 기사를 폭행한 사건이 여러 번 발생하였다. 이에 기계가 마스크를 쓰지 않은 사람을 가려내고 마스크를 쓰라고 한다면 버스 기사에게 향하는 비이성적 분노가 줄어들 것이라고 생각하였다. 따라서, 본 논문에서는 Keras와 같은 기본적인 기계 학습 패키지를 사용하여 빠르고 정확하게 마스크의 착용여부를 확인할 수 있는 방식을 제안한다. 제안된 방식은 고성능 컴퓨터 및 그래픽카드의 필요없이 CPU에서만 작동하는 마스크 착용 판별프로그렘으로, 추가적으로 알림을 보낼 수 있는 웹사이트와 음성 경고 시스템도 함께 구현하였다. 이 방법은 테스트 데이터셋에서 99.5% 이상의 정확도를 달성했고, GPU가 아닌 CPU에서 6fps 정도의 속도를 지원하여 실생활에 사용될 수 있다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.200-209
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• 2019

When forecasting future values, a model estimated after minimizing training errors can yield test errors higher than the training errors. This result is the over-fitting problem caused by an increase in model complexity when the model is focused only on a given dataset. Some regularization and resampling methods have been introduced to reduce test errors by alleviating this problem but have been designed for use with only a given dataset. In this paper, we propose a new optimization approach to reduce test errors by transforming a test error minimization problem into a training error minimization problem. To carry out this transformation, we needed additional data for the given dataset, termed pseudo data. To make proper use of pseudo data, we used three types of missing data imputation techniques. As an optimization tool, we chose the least squares method and combined it with an extra pseudo data instance. Furthermore, we present the numerical results supporting our proposed approach, which resulted in less test errors than the ordinary least squares method.

• Journal of Information Management
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• v.37 no.3
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• pp.139-163
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• 2006

This article analyze a constructing process of photographic digital archive based on photographs of korean democratization movements. After photographic digital archive defines as integrated systems in which photographic digital objects collect, classify, describe, preserve and access, this article explains metadata elements and classification schema reflecting a special quality of photograph. Especially, this article presents dynamic classification structure using subject keywords. After all, this method provides integrity and interrelationship with photographes which promote usability of photographic digital objects.

• Journal of Information Management
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• v.34 no.3
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• pp.1-19
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• 2003

Due to rapid internet supply, the increase of electronic resources including network resources, and the appearance of metadate, there has been a sudden change in cataloguing fields. To deal with rapid changes, the related organizations such as IFLA are organizing the research team and revising rules. This study analyzes current trend of cataloguing digital resources, considering recent matters in regards with the revision of ISBD(ER), AACR2R 2002 edition, MARC 21, Dubin Core, KCR4 etc. This research discusses also the questions of how digital resources are organized; automatic indexing, matadata, and MARC format.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.5
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• pp.513-521
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• 2022

This study deals with an AI architecture model for collecting battlefield data using the tactical C4I system. Based on this model, the artificial staff can be utilized in tactical echelon. In the current structure of the Army's tactical C4I system, Servers are operated by brigade level and above and divided into an active and a standby server. In this C4I system structure, the AI server must also be installed in each unit and must be switched when the C4I server is switched. The tactical C4I system operates a server(DB) for each unit, so data matching is partially delayed or some data is not matched in the inter-working process between servers. To solve these issues, this study presents an operation concept so that all of alternate server can be integrated based on virtualization technology, which is used as an source data for AI Meta DB. In doing so, this study can provide criteria for the AI architectural model of the ground tactical echelon.