• Convergence Security Journal
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• v.23 no.2
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• pp.85-94
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• 2023

In the face of the serious security situation with the increasing threat from North Korea, Korean Army is pursuing a reduction in troops through the performance improvement project of the GOP science-based border security system, which utilizes advanced technology. In order for the GOP science-based border security system to be an effective alternative to the decrease in military resources due to the population decline, it must guarantee a high detection and identification rate and minimize troop intervention by dramatically improving the false detection rate. Recently introduced in Korean Army, the GOP science-based border security system is known to ensure a relatively high detection and identification rate in good weather conditions, but its performance in harsh weather conditions such as rain and fog is somewhat lacking. As an alternative to overcoming this, a radar-based border security system that can detect objects even in bad weather has been proposed. This paper proves the effectiveness of the AI-based scientific border security system based on radar that is being currently tested at the 00th Division through the 2021 Rapid Acquisition Program, and suggests the direction of development for the GOP scientific border security system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.693-698
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• 2018

This paper proposes a vehicle detection method for parking areas using unmanned aerial vehicles (UAVs) and using YOLOv2, which is a recent, known, fast, object-detection real-time algorithm. The YOLOv2 convolutional network algorithm can calculate the probability of each class in an entire image with a one-pass evaluation, and can also predict the location of bounding boxes. It has the advantage of very fast, easy, and optimized-at-detection performance, because the object detection process has a single network. The sliding windows methods and region-based convolutional neural network series detection algorithms use a lot of region proposals and take too much calculation time for each class. So these algorithms have a disadvantage in real-time applications. This research uses the YOLOv2 algorithm to overcome the disadvantage that previous algorithms have in real-time processing problems. Using Darknet, OpenCV, and the Compute Unified Device Architecture as open sources for object detection. a deep learning server is used for the learning and detecting process with each car. In the experiment results, the algorithm could detect cars in a dense area using UAVs, and reduced overhead for object detection. It could be applied in real time.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.11-14
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• 2002

게임에서 충돌탐지는 게임의 성능향상을 위해 중요하다. 본 논문에서는 효율적인 충돌탐지를 위해 BSP 트리를 사용한다. 공격에 사용되는 스프라이트와 공격의 대상이 되는 스프라이트를 트리로 구성하여 빠른 시간내에 충돌탐지를 행한다. 또한 스프라이트의 모양에 따라 경계 볼륨(bounding volume)을 구와 박스(box)를 선택적으로 사용하여 충돌탐지에서 발생하는 문제점을 해결한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.715-717
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• 2022

객체 탐지는 디지털 이미지나 비디오에서 유의미한 객체를 탐지하는 작업을 말한다. 이 작업은 객체가 있는 곳에 경계상자를 그리는 Localization과 객체의 Class를 구분하는 Classification 이 두 단계로 나눌 수 있는데, 각각의 단계를 순차적으로 행하는 2-stage detection 방식과 동시에 행하는 1-stage detection 방식을 실시간으로 마스크 착용여부를 탐지하는 프로그램에 적용하면서 속도와 성능을 비교하고 어떤 방식이 적합한지 연구한다

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.404-406
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• 2003

C언어는 포인터형 변수를 제공하며 배열의 경계를 인식하지 않는다. 이러한 특성에서 기인한 버퍼넘침 (buffer overflew)은 널리 알려진 취약점으로서 보안침해 수단으로 널리 악용되고 있다. 이 문제를 해결하기 위한 한 방법으로 오용탐지기술은 버퍼넘침에 공통적으로 사용되는 시그너쳐(Signature)를 가지고 클라이언트(client)가 전송한 패킷을 검사함으로서 고전적인 버퍼넘침을 탐지하고 있다. 본 논문에서는 이러한 탐지 방법을 우회할 수 있는 보다 위협적이고 지능적인 보안침해 가능성을 제시한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.1217-1220
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• 2004

현재까지 IDS는 관리자의 개입 없이는 효과적인 운용이 불가능하고, IDS를 사용하더라도 여전히 침입 발생 가능성이 있고, 다양한 우회 가능성이 존재한다. 본 논문에서는 기존에 제안된 침입탐지 시스템을 분석하고, C-Box에 규정된 정책을 이용한 데이타소스 기반의 침입탐지 시스템을 설계하여 이를 실험하였다. 본 연구는 데이터 소스 기반에서 침입 탐지 방법 기준의 비정상적인 형태에 의한 탐지와 오류에 의한 탐지기법을 적용하였으며, IDS에 침입 탐지 정책을 설계하였고, 규정에 의한 정책중심의 침입탐지 기법을 정상적인 동작과 비정상적인 동작을 구분하는 경계를 정의한다. 또한, 침입탐지 정책을 이용한 호스트기반 IDS를 설계하고 구현함으로서 정보시스템의 취약성을 보완할 수 있었다. 침입탐지 실험을 위한 시스템 호출 기술은 커널에 프로세스들의 특성을 자세하게 정의하고, 이를 실행할 수 있도록 기반을 구축함으로서 가능하게 하였다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.76-84
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• 2014

As the number of high-density videos increase, parallel processing approaches are necessary to process a large-scale of video data. When a processing method of video data requires thousands of simple operations, GPU-based parallel processing is preferred to CPU-based parallel processing by way of reducing the time and space complexities of a given computation problem. This paper studies the parallel design and implementation of a shot-boundary detection algorithm. The proposed shot-boundary detection algorithm uses pixel brightness comparisons and global histogram data among the blocks of frames, and the computation of these data is characterized with the high parallelism for the related operations. In order to maximize these operations in parallel, the computations of the pixel brightness and histogram are designed in parallel and implemented in NVIDIA GPU. The GPU-based shot detection method is tested with 10 videos from the set of videos in National Archive of Korea. In experiments, the detection rate is similar but the computation time is about 10 time faster to that of the CPU-based algorithm.

• Journal of KIISE
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• v.44 no.8
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• pp.774-781
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• 2017

Mention detection systems use nouns or noun phrases as a head and construct a chunk of text that defines any meaning, including a modifier. The term "mention detection" relates to the extraction of mentions in a document. In the mentions, a coreference resolution pertains to finding out if various mentions have the same meaning to each other. A pointer network is a model based on a recurrent neural network (RNN) encoder-decoder, and outputs a list of elements that correspond to input sequence. In this paper, we propose the use of mention detection using pointer networks. Our proposed model can solve the problem of overlapped mention detection, an issue that could not be solved by sequence labeling when applying the pointer network to the mention detection. As a result of this experiment, performance of the proposed mention detection model showed an F1 of 80.07%, a 7.65%p higher than rule-based mention detection; a co-reference resolution performance using this mention detection model showed a CoNLL F1 of 52.67% (mention boundary), and a CoNLL F1 of 60.11% (head boundary) that is high, 7.68%p, or 1.5%p more than coreference resolution using rule-based mention detection.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.30-39
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• 2007

This paper is concerned with development of system to detect an obstructive vehicle which is an essential prerequisite for autonomous driving system of ASV(Advanced Safety Vehicle). First, the boundary of driving lanes is detected by a Kalman filter through the front image obtained by a CCD camera. Then, lanes are recognized by regression analysis of the detected boundary. Second, parameters of road curvature within the detected lane are used as input in error-BP algorithm to recognize the driving direction. Finally, an obstructive vehicle that enters into the detection region can be detected through setting detection fields of the front and lateral side. The experimental results showed that the proposed system has high accuracy more than 90% in the recognition rate of driving direction and the detection rate of an obstructive vehicle.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.35-42
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• 2013

Shot boundary detection is an essential step for efficient browsing, sorting, and classification of video data. Robust shot detection method should overcome the disturbances caused by pixel brightness and object movement between frames. In this paper, two shot boundary detection methods are presented to address these problem by using segmentation, object movement, and pixel brightness. The first method is based on the histogram that reflects object movements and the morphological dilation operation that considers pixel brightness. The second method uses the pixel brightness information of segmented and whole blocks between frames. Experiments on digitized video data of National Archive of Korea show that the proposed methods outperforms the existing pixel-based and histogram-based methods.