• 정보처리학회논문지B
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• 제12B권4호
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• pp.443-450
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• 2005

디지털 핑거프린팅은 워터마킹 기술에 기반한 콘텐츠 보호 기술로 디지털 콘텐츠에 구매자의 정보인 핑거프린트를 삽입하는 기술을 말한다. 콘텐츠 안에 삽입된 핑거프린트 정보는 다양한 공격을 받게 된다. 특히 동일한 콘텐츠 안에 서로 다른 구매자의 정보를 넣게 되는 핑거프린팅의 특성으로 인해 공모 공격이 가능하고, 그 중 평균화 공격은 빠르고 효과적인 공모 공격에 해당한다. 본 논문에서는 평균화 공격에 강인한 비디오 핑거프린팅 시스템을 제안한다. 공모 공격 후에도 특정 위치의 코드 값이 원래의 값을 유지할 수 있는 공모 방지 코드를 적용하였다. 또한 사용자의 수가 늘어남에 따라 핑거프린트 코드의 길이가 증가하는데 이와 같은 코드의 효율적인 삽입 및 검출을 위해 제로기반 코드 변조 기법을 적용함으로써 올바른 공모자 추적이 가능하도록 하였다. 제안한 방식을 사용하여 원본 비디오를 사용하지 않는 비디오 핑거프린팅 시스템을 구현하였고, 다양한 공모자의 수에 따른 실험을 수행하였다. 실험 결과에 따르면 대부분의 경우 공모자를 올바르게 추출할 수 있었고, 최소 한 명 이상의 공모자를 성공적으로 검출할 수 있었다.

• Smart Structures and Systems
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• 제12권1호
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• pp.73-95
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• 2013

Nine deoxyribonucleic acid (DNA) sequences were used to functionalize single-walled carbon nanotube (SWNT) sensors to detect the trace amount of methanol, acetone, and HCl in vapor. DNA 24 Ma (24 randomly arranged nitrogenous bases with one amine at each end of it) decorated SWNT sensor and DNA 24 A (only adenine (A) base with a length of 24) decorated SWNT sensor have demonstrated the largest sensing responses towards acetone and HCl, respectively. On the other hand, for the DNA GT decorated SWNT sensors with different sequence lengths, the optimum DNA sequence length for acetone and HCl sensing is 32 and 8, separately. The detection of methanol, acetone, and HCl have identified that DNA functionalized SWNT sensors exhibit great selectivity, sensitivity, and repeatability with an accuracy of more than 90%. Further, a sensor array composed of SWNT functionalized with various DNA sequences was utilized to identify acetone and HCl through pattern recognition. The sensor array is a combination of four different DNA functionalized SWNT sensors and two bare SWNT sensors (work as reference). This wireless sensing system has enabled real-time gas monitoring and air quality assurance for safety and security.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권2호
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• pp.131-139
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• 2016

Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.

• Journal of Biosystems Engineering
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• 제37권1호
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• pp.65-74
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• 2012

Purpose: An autonomous robot was developed for harvesting strawberries cultivated in bench-type systems. Methods: The harvest robot consisted of four main components: an autonomous vehicle, a manipulator with four degrees of freedom (DOF), an end effector with two DOFs, and a color computer vision system. Strawberry detection was performed based on 3D image and distance information obtained from a stereo CCD color camera and a laser device, respectively. Results: In this work, a Cartesian type manipulator system was designed, including an intermediate revolute axis and a double driven arm-based joint axis, so that it could generate collision-free motions during harvesting. A DC servomotor-driven end-effector, consisting of a gripper and a cutter, was designed for gripping and cutting the strawberry stem without damaging the strawberry itself. Real-time position tracking algorithms were developed to detect, recognize, trace, and approach strawberries under natural light conditions. Conclusion: The developed robot system could harvest a strawberry within 7 seconds without damage.

• 응용통계연구
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• 제31권1호
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• pp.155-165
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• 2018

특이점 및 영향점은 자료분석을 하는 데 사용되는 수치적 측도들을 왜곡한다. 각종 자료분석에 있어서의 특이점 검색을 위한 검정 통계량이나 그림도구에 관한 연구는 꾸준히 전개되어 왔다. Jang과 Anderson-Cook (Quality and Reliability Engineering International, 30, 1409-1425, 2014)은 불꽃그림이란 이름을 붙인 그림도구를 발표하였는데 특이점이나 영향점이 일변량/이변량 자료분석 및 회귀분석에 어떠한 영향을 미치는지 알기 위하여 3-D 불꽃그림 및 불꽃그림 행렬을 제시하였다. 본 연구에서는 이러한 불꽃그림이 통계적 추론에서 특이점을 찾아내고 이러한 특이점의 영향을 평가하기 위한 탐색적 자료분석 그림도구로서 사용될 수 있음을 보였다.

• 한국정보전자통신기술학회논문지
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• 제4권1호
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• pp.17-22
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• 2011

본 연구에서는 부정맥 판독을 위해 심전도 신호로부터 P파의 강조 및 검출 방법을 제안하였다. 이 방법에서는 고전적인 필터나 이동평균필터를 사용하지 않고 하강 기울기의 변곡점을 검출하기에 효과적인 하강 기울기 추적파를 이용하여 고주파 잡음을 무시해 주는 필터 용도로 사용함과 동시에 진폭이 낮고 불명확한 P파와 T파를 강조해 줌으로써 이들의 검출을 용이하게 하였다. 이 강조 및 검출 방법을 MIT/BIH 데이터에 적용하여 구현하였으며 그 실용성을 확인하였다.

• 한국정보처리학회논문지
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• 제6권11S호
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• pp.3410-3419
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• 1999

Autonomous Intrusion Analysis Agent(AIAA) is Incident Response Team staff's tool that scans, analyses, reports and alerts the traces of intrusion based on system logs and intruder's backdoors inside compromised system by IR staff after security incident is reported to the IR team. AIAA is intelligent to recognize to check out who is intruder from all the user accounts and to report the suspected candidates to the master control system in IR team. IR staff who controls AIAA with master system can pick up an intruder from the candidates reported by AIAA agent and review all related summary reports and details including source host's mane, finger information, all illegal behavior and so on. AIAA is moved to compromised system by the staff to investigate the signature of intrusion along the trace of victim hosts and it is also operated in secret mode to detect the further intrusion. AIAA is alive in all victim systems until the incident is closed and IR staff can control AIAA operation and dialogue with AIAA agent in Web interface.

• Bulletin of the Korean Chemical Society
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• 제32권8호
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• pp.2732-2736
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• 2011

In pH 4.2 HAc-NaAc buffer solution, horseradish peroxidase (HRP) catalyzed $H_2O_2$ oxidation of nanosilver to form $Ag^+$. After centrifugation, $Ag^+$ in the supernatant can be measured by graphite furnace atomic absorption spectrophotometry (GFAAS) at the silver absorption wavelength of 328.1 nm. When HRP concentration increased, the $Ag^+$ concentration in the supernatant increased, and the absorption value enhanced. The HRP concentration in the range of 0.84-50 $ng{\cdot}mL^{-1}$ was linear to the enhanced absorption value (${\Delta}A$), with a regression equation of ${\Delta}A$=0.012C+0.11, correlation coefficient of 0.9988, and detection limit of 0.41 $ng{\cdot}mL^{-1}$ HRP. The proposed GFAAS method was used to detect HRP in waste water samples, with satisfactory results.

• Bulletin of the Korean Chemical Society
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• 제35권12호
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• pp.3547-3552
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• 2014

Laser Raman spectroscopy is one of the most powerful technologies for standoff detection of hazardous materials including explosives. Supported by recent development of laser and sensitive ICCD camera, the technology can identify trace amount of unknown substances in a distance. Using this concept, we built a standoff detection system, in which nanosecond pulse laser and nanosecond gating ICCD technique were delicately devised to avoid the large background noise which suppressed weak Raman signals from the target sample. In standoff detection of explosives which have large kill radius, one of the most important technical issues is the detection distance from the target. Hence, we focused to increase the detection distance up to 54 m by careful optimization of optics and laser settings. The Raman spectra of hazardous materials observed at the distance of 54 m were fully identifiable. We succeeded to detect and identify eleven hazardous materials of liquid or solid particles, which were either explosives or chemical substances used frequently in chemical plants. We also performed experiments to establish the limit of detection (LOD) of HMX at 10 m, which was estimated to be 6 mg.

• 한국컴퓨터정보학회논문지
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• 제15권11호
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• pp.109-116
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• 2010

본 논문에서는 가상과 현실을 이어주는 하나의 방법인 마커(Marker)인식방법에서 멀티마커방법을 사용하여 복잡한 환경이나 또는 손으로 가려지는 현상에서도 추적이 가능한 기법을 제안한다. 기존의 마커인식 방법은 한 개의 마커에만 의존하기 때문에 마커가 가려짐 현상에서 객체가 깜박이는 현상을 볼 수 있는데 이 시스템에서 마커가 가려지더라도 여러 개의 마커가 가려진 마커를 대신함으로써 증강된 객체가 나타남을 확인 할 수 있다. 이러한 기술을 구현하기 위해 관점 매트릭스(perspective matrix)를 이용한 멀티 마커 추정 방법으로 다른 연산이 없기 때문에 빠르게 추정 할 수 있는 것이 특징이다. 구현된 기술은 영상으로부터 다양한 환경에서 마커를 인지하고 위치를 추정하는 기술로써 다양한 환경에서 3차원 객체를 표현 할 수 있다.