• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.7
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• pp.582-589
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• 2012

This paper describes the algorithm recognizing car type from the image received from UAV and the recognition results between three types of car images. Using the NCC(Normalized Cross-Correlation) algorithm, geometric information is matched from template images. Template images are obtained from UAV and satellite map and indoor experiment is performed using satellite map. After verification of the possibility, experiment for verification of same car type recognition is performed using small UAV. In the experiment, same type cars are matched with 0.6 point similarity and truck with similar color distribution is not matched with template image of a sedan.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.1-18
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• 2017

Recently, identification of the extremely stressed condition of children is an essential skill for real-time recognition of a dangerous situation because incidents of children have been dramatically increased. In this paper, therefore, we present a model based on machine learning techniques for stress status identification of a child by using bio-signals such as voice and heart rate that are major factors for presenting a child's emotion. In addition, a smart band for collecting such bio-signals and a mobile application for monitoring child's stress status are also suggested. Specifically, the proposed method utilizes stress patterns of children that are obtained in advance for the purpose of training stress status identification model. Then, the model is used to predict the current stress status for a child and is designed based on conventional machine learning algorithms. The experiment results conducted by using a real-world dataset showed that the possibility of automated detection of a child's stress status with a satisfactory level of accuracy. Furthermore, the research results are expected to be used for preventing child's dangerous situations.

• Journal of Intelligence and Information Systems
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• v.5 no.2
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• pp.43-61
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• 1999

국방정보체계는 종류가 다양하고 대규모로 추진되기 때문에 소프트웨어 개발을 위한 사업관리에 어려움이 많이 따른다. 더구나, 현재 국방정보체계의 사업관리는 자동화시스템을 이용하여 체계적으로 실시하지 않고 수동으로 처리하기 때문에 많은 문제점이 야기되고 있고 현재의 소프트웨어 개발 표준인 ISO 12207을 수정 없이 그대로 대규모 실시간 국방정보체계의 소프트웨어 개발사업에 적용하는 것도 한계가 있다. 따라서 본 논문의 목적은 효율적인 국방정보체계 소프트웨어 개발 사업관리를 위해서 국방정보체계별로 적절한 개발 생명주기 모델을 선정하고, 각 단계별로 사업관리자가 수행할 태스크를 식별하는 등의 도메인 지식을 개발하여 궁극적으로 국방정보체계 사업관리용 전문가 시스템을 개발할 때 활용함과 동시에 국방정보체계 사업관리의 일환으로 최종 산출물에 대한 품질을 높일 수 있도록 체계적인 시험평가 방안에 대한 도메인 지식도 개발하여 사업관리용 전문가 시스템 구축 시에 활용하고자 하는 것이다. 이를 위해서 국방정보체계의 소프트웨어 개발 사업관리에 영향을 미치는 요소들을 식별하고 이를 이용하여 국방정보체계 소프트웨어 개발 사업관리를 위한 전문가 시스템을 구축할 수 있도록 생명주기 모델 선정을 위한 도메인 지식, 생명주기 각 단계별 세부활동을 위한 도메인 지식, 시험평가 방안선정을 위한 도메인 지식을 개발하여 제시하였다. 본 논문에서는 이러한 도메인 지식을 전문가 시스템 개발도구에서 지원되는 각종 에디터에 사용되는 형태나 IF A, THEN B 형태로 개발하지 않고 사업관리용 전문가 시스템의 지식베이스에 포함될 지식들을 식별하는 것을 중심으로 개발하였다. 즉, 본 논문에서는 국방정보체계 사업관리용 전문가 시스템 개발의 필요성과 가능성을 검증하는 용역연구과제로써 프로토타입 혹을 완벽한 사업관리용 전문가 시스템을 개발한 것이 아니며, 전문가 시스템 개발 시 가장 어렵고 중요한 지식베이스 모듈속에 포함될 도메인 지식을 개발하는 것이 이 연구의 목적이다. 이러한 연구는 궁극적으로는 이러한 도메인 지식이 국방정보체계의 사업관리를 위한 전문가 시스템의 지식베이스 모듈 구축 시 기초/기반 및 핵심 지식으로 활용될 수 있을 것이다.

• Journal of Korean Institute of Fire Investigation
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• v.10 no.1
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• pp.37-45
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• 2007

In this paper, the possibility of arc tracking, caused by combustion at the circuit breaker, was discussed. The arc tracking, occurred at the source terminals of all the circuit breakers, when we burned electric leakage circuit breakers with 220V applied. We had a same results of the experiment to simulate fire scene, in the circumstance of fire, all of the circuit breakers had arc tracking caused by combustion. Therefore we confirmed that the arc tracking at the source terminal of circuit breaker could be occurred by just combustion in the fire scene, and it was impossible to decide the cause of fire for reason of discriminating arc tracking at the terminals of circuit breaker.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.9
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• pp.767-773
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• 2018

In many cases, modern aircraft are equipped with data acquisition system which checks the structural integrity of the aircraft. The analysis of the vibration data collected with the system is generally performed in dependence on a skilled expert who is familiar with aircraft design. Therefore, it is difficult to provide a representative and objective defect identification standard for general users. In this paper, we shows that it is possible to identify the type of maneuvers and faults by using the Principal Component Analysis(PCA) method in the vast vibration data collected during aircraft operation without using the existing aircraft design analysis. We classified the ROK Army aircraft vibration data for maneuvers and faults types, and applied the PCA to the classified data. Our result shows that it is possible to develop an objective maneuver/fault identification method without design analysis for general users.

• Journal of Industrial Convergence
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• v.17 no.3
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• pp.23-29
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• 2019

Object recognition distinguish objects which are different from each other. But Face recognition distinguishes Identity of Faces with Similar Patterns. Feature extraction algorithm such as LBP, HOG, Gabor is being replaced with Deep Learning. As the technology that identify individual face with machine learning using Deep Learning Technology is developing, The Face Recognition Technology is being used in various field. In particular, the technology can provide individual and detailed service by being used in various offline environments requiring user identification, such as Smart Mirror. Face Recognition Technology can be developed as the technology that authenticate user easily by device like Smart Mirror and provide service authenticated user. In this paper, we present investigation about Face Recognition among various techniques for user authentication and analysis of Python source case of Face recognition and possibility of various service using Face Recognition Technology.

• Convergence Security Journal
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• v.24 no.2
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• pp.189-200
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• 2024

In recent years, the development of defense technology has become digital with the introduction of advanced assets such as drones equipped with artificial intelligence. These assets are integrated with modern information technologies such as industrial IoT, artificial intelligence, and cloud computing to promote innovation in the defense domain. However, the convergence of the technology is increasing the possibility of transfer of cyber threats, which is emerging as a problem of increasing the vulnerability of defense assets. While the current cybersecurity methodologies focus on the vulnerability of a single asset, interworking of various military assets is necessary to perform the mission. Therefore, this paper recognizes these problems and presents a mission-based asset management and evaluation methodology. It aims to strengthen cyber security in the defense sector by identifying assets that are important for mission execution and analyzing vulnerabilities in terms of cyber security. In this paper, we propose a method of classifying mission dependencies through linkage analysis between functions and assets to perform a mission, and identifying and classifying assets that affect the mission. In addition, a case study of identifying key assets was conducted through an attack scenario.

• Journal of the korean academy of Pediatric Dentistry
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• v.48 no.3
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• pp.245-254
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• 2021

The aim of this study was to evaluate the accuracy of 3 different automatic landmark identification programs on lateral cephalgrams and the clinical acceptability in pediatric dentistry. Sixty digital cephalometric radiographs of 7 to 12 years old healthy children were randomly selected. Fourteen landmarks were chosen for assessment and the mean of 3 measurements of each landmark by a single examiner was defined as the baseline landmarks. The mean difference between an automatically identified landmark and the baseline landmark was measured for each landmark on each image. The total mean difference of 3 automatic programs compared to the baseline landmarks were 2.53 ± 1.63 mm. Errors among 3 programs were not significantly different for 12 of 14 landmarks except Orbitale and Gonion. The automatic landmark identification programs showed significant higher mean detection errors than the manual method. The programs couldn't be used as the 1st tool to replace human examiners. But considering short consuming time, these results indicate that all 3 programs have sufficient validity to be used in pediatric dental clinic.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.492-497
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• 2019

This study analysed the legal concept of personal information(PI), which was not differentiated from behavioral information, and established it clearly for invigorating online targeted advertising(OTA), which draw attention in big data era; by selecting Guidelines of Assessment of Data Breach Incident Factors and Guidelines of Measures for No-Identifying Personal Information based on Personal Information Protection Act(PIPA) and Enforcement Decree of the PIPA. As a result, PI was defined as any kind of information relating to (1)a living individual(not group, corporate body or things etc.); (2)makes possibly identify the individual by his or her identifiers such as name, resident registration number, image, etc. (not included if not identify the individual); and (3)including information like attribute values which makes possibly identify any specific individual, if not by itself, but combined with other information which can be actually collected and combined). Specifically, PI includes basic, proper distinguishable, sensitive and other PI. It is suggested that PI concept should be researched continually with digital technology development; the effectiveness of the Guidelines of PI Protection in OTA, the legal principles of PI protection from not only users' but business operators' perspectives and the differentiation between PI and behavioral information in OTA should be researched.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.566-572
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• 2021

A methodology for the identification and coordinates estimation of air cavities under urban ground or sandy soil using its natural poles and natural resonant frequencies is presented. The potential of this methodology was analyzed. Simulation models of PEC (Perfect Electric Conductor)s with various shapes and dimensions were developed using an EM (Electromagnetic) simulator. The Cauchy method was applied to the obtained EM scattering response of various objects from EM simulation models. The natural poles of objects corresponding to its instinct characterization were then extracted. Thus, a library of poles can be generated using their natural poles. The generated library of poles provided the possibility of identifying a target by comparing them with the computed natural poles from a target. The simulation models were made assuming that there is an air cavity under urban ground or sandy soil. The response of the desired target was extracted from the electromagnetic wave scattering data from its simulation model. The coordinates of the target were estimated using the time delay of the impulse response (peak of the impulse response) in the time domain. The MP (Matrix Pencil) method was applied to extract the natural poles of a target. Finally, a 0.2-m-diameter spherical air cavity underground could be estimated by comparing both the pole library of the objects and the calculated natural poles and the natural resonant frequency of the target. The computed location (depth) of a target showed an accuracy of approximately 84 to 93%.