• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.5
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• pp.1003-1008
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• 2017

The Internet of Things has attracted considerable research attention in recent years. In order for the new IoT technology to be widely used, the reliability and protection of information is required. IoT systems are very vulnerable to physical security due to their easy accessibility. Along with the development of SoC technology, many operating systems have been developed and many new operating systems have been introduced. In this paper, we describe the vulnerability analysis results for operating systems running on the ARMv7 Thumb Architecture hardware platform. For the recently introduced "Windows 10 IoT Core" operating system, I implemented the Zero-Day Attack by implanting the penetration code developed through the research into a specific IoT system. The virus detection test for the resulting penetration code was validated by referral to the "virustotal" site.

• Journal of KIISE
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• v.44 no.10
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• pp.1094-1104
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• 2017

In this paper, a motion image is detected based on the irregularity of lights. This motion image is extracted by modifying the reflected light region of the 3 way-diff algorithms. 3 way-diff algorithm extracts reflected light region using the 3-successive image. In this algorithm, The reflected light region is a region generated by light in the image production process and is finally created around all objects. The algorithm shows a process to extracting the region. This process is a simple operation, but doesn't have a defined formula for light. This paper judges that the reflected light region is a kind of noise at the 3 way-diff algorithms and defines the formula for extracting the reflected light region. It shows that compared with the proposed algorithm and existing algorithm through experiment.

• The Korean Journal of Ecology
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• v.22 no.5
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• pp.255-263
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• 1999

Entomopathogenic nematodes were isolated through the investigation of soil samples from various biotopes in south Korea, the efficiency of isolation for highly pathogenic nematodes to silkworms (Bombyx mori) was as high as 28 %. Twenty-eight strains of nematodes, selected among 100 samples by silkworms were confirmed the pathogenicity, multiplicity, and tolerance against various condition of preservation. Pathogenicity of the nematode isolates to agricultural and environmental pests such as Calliphora vomitoria, Pseufaletia separata, Palomena angulosa, and Melolontha incana were high. Mortality was varied from 20 to 100% by the pest insects and nematode strains. The high detectablity of entomopathogenic nematodes resulted from the methods of collection for soil samples within 10 cm depth after eliminating dried soil surface and the use of silkworm trap. High population of entomopathogenic nematodes represented the strong activity and broad action radius in the environment. Most of the nematode isolates were successfully cultured on the silkworm host as well as on artificial media, and proved their potential for the use of biological control agent.

• Journal of Broadcast Engineering
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• v.22 no.2
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• pp.182-192
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• 2017

In this paper, we propose hand tracking system combined with color and motion information. Most of hand detection and tracking systems are performed by modeling skin color. However, in this approach, since it is highly influenced by light or surrounding objects, accurate values cannot be derived constantly. Also, depending on the skin color, hand tracking may be interrupted by not only the hand but also the background with a color similar to that of the face and skin. Therefore, we design the hand tracking that can effectively track a hand by using motion history image(MHI) and combining it with CamShift. The proposed system is implemented based on C/C++, and the experiments proved that the proposed method shows stable and excellent performance.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.685-697
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• 2020

Immersive media videos, such as panorama and 360-degree videos, must provide a sense of realism as if the user visited the space in the video, so they should be able to represent the reality of the real world. However, in panorama and 360-degree videos, objects appear to overlap or disappear due to parallax between cameras, and such parallax distortion may interfere with immersion of the user's content. Accordingly, although many video stitching algorithms have been proposed to overcome parallax distortion, parallax distortion still occurs due to the low performance of the Object detection module and limitations of the Seam generation method. Therefore, this paper analyzes the limitations of the existing video stitching technology and proposes a method for detecting and correcting parallax distortion of video stitching using the LDPM (Local Differential Pixel Mean) image evaluation method that overcomes the limitations of the video stitching technique.

• Convergence Security Journal
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• v.22 no.2
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• pp.21-26
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• 2022

Wearing face masks is an effective measure to prevent COVID-19 infection. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging. Recently introduced MTCNN (Multi-task Cascaded Convolutional Networks)presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask MTCNN is an algorithm that extends MTCNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. It is easy to generalize the R-CNN to other tasks. In this paper, we proposed an infrared image detection algorithm based on R-CNN and detect heating elements which can not be distinguished by RGB images.

• The Korean Journal of Zoology
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• v.31 no.2
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• pp.142-146
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• 1988

Metabolism of benzo(a)pyrene, the most thoroughly studied PAH, was studied in mouse placental microsomes incubated with $^3$H-labeled B(a)P. B(a)P metabolites were separated using HPLC fitted with a C18- $\mu$ Bondapak column. The single major metabolite by mouse placental microsomes induced by B(a)P was 7, 8-diol B(a)P, while 4, 5-diol B(a)P, 3-OH and quinones constituted minor metabolites. Treatment with 3-methyl-cholanthrene to mice resulted in indudion of hydroxy B(a)P and quinone compounds. Phenobarbital treated mouse placental microsomes also showed elevated level of B(a)P metabolism with 7, 8-diol B(a)P as a major metabolite.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.497-502
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• 2016

Shape Contexts Recognition(SCR) is a technology recognizing shapes such as figures and objects, greatly supporting technologies such as character recognition, motion recognition, facial recognition, and situational recognition. However, generally SCR makes histograms for all contours and maps the extracted contours one to one to compare Shape A and B, which leads to slow progress speed. Thus, this paper has made simple yet more effective algorithm with optimized contour, finding the outlines according to shape figures and using the improved Douglas-Peucker algorithm and Harris corner detector. With this improved method, progress speed is recognized as faster.

• Journal of IKEEE
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• v.25 no.1
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• pp.83-87
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• 2021

In harsh environments such as fog or fine dust, the cameras' detection ability for object recognition may significantly decrease. In order to accurately obtain important information even in bad weather, fog removal algorithms are necessarily required. Research has been conducted in various ways, such as computer vision/data-based fog removal technology. In those techniques, estimating the amount of fog through the input image's depth information is an important procedure. In this paper, a linear model is presented under the assumption that the image dark channel dictionary, saturation ∗ value, and sharpness characteristics are linearly related to depth information. The proposed method of haze removal through a linear model shows the superiority of algorithm performance in quantitative numerical evaluation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.270-272
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• 2020

본 논문은 코로나 방역의 자동화를 위한 Deep learning 기술 적용에 대해 연구한다. 2020년에 가장 중요한 이슈 중 하나인 COVID-19와 그 방역에 대해 많은 사람들이 IT분야에서 떠오르고 있는 artificial intelligence(AI)에 주목하고 있다. COVID-19로 인해 마스크 착용이 선택이 아닌 필수가 되며, 이를 통제하기 위한 모델이 필요한 상황이다. AI, 그 중에서도 Deep learning의 Object detection 기술을 일상생활 곳곳에 존재하는 영상 장치들에 적용하여 합리적인 비용으로 방역의 실시간 자동화를 구현할 수 있다. 이번 논문에서는 인터넷에 공개되어 있는 사물인식 오픈소스를 활용하여 이를 구현하기 위한 연구를 진행하였다. 또 이를 위한 Dataset 확보에 대한 조사도 진행하였다.