• 한국군사과학기술학회지
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• 제14권6호
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• pp.1002-1008
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• 2011

The mine-detection system, which is one of the various mission equipments for Ground Vehicle System, detects mine under the ground. The mine detection sensors comprised of Metal Detection(MD) sensor and Ground Penetration Radar(GPR) are attached on the end of the multi-DOF manipulator. The manipulator moves the sensor to sweep mine areas keeping the pre-determined distance between the sensor and ground to enhance mine detection performance. The detection system can be operated automatically, semi-automatically and manually. When the detection system is operated automatically, the sensor should avoid collisions with unexpected obstacles which may exist on the ground. Two types of ultra-sonic sensors were developed for the mine detection sensor system to keep the appropriate gap between sensor and the ground to avoid the obstacles. Also, mine place marking device was developed.

• Journal of Information Processing Systems
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• 제1권1호
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• pp.14-21
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• 2005

Even though mainly statistical methods have been used in anomaly network intrusion detection, to detect various attack types, machine learning based anomaly detection was introduced. Machine learning based anomaly detection started from research applying traditional learning algorithms of artificial intelligence to intrusion detection. However, detection rates of these methods are not satisfactory. Especially, high false positive and repeated alarms about the same attack are problems. The main reason for this is that one packet is used as a basic learning unit. Most attacks consist of more than one packet. In addition, an attack does not lead to a consecutive packet stream. Therefore, with grouping of related packets, a new approach of group-based learning and detection is needed. This type of approach is similar to that of multiple-instance problems in the artificial intelligence community, which cannot clearly classify one instance, but classification of a group is possible. We suggest group generation algorithm grouping related packets, and a learning algorithm based on a unit of such group. To verify the usefulness of the suggested algorithm, 1998 DARPA data was used and the results show that our approach is quite useful.

• 대한교통학회:학술대회논문집
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• 대한교통학회 1995년도 제28회 학술발표회
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• pp.77-107
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• 1995

This paper documents the development of a fuzzy logic based incident detection model for urban diamond interchanges. Research in incident detection for intersections and arterials is at a very initial stage. Existing algorithms are still far from being robust in dealing with the difficulties related with data availability and the multi-dimensional nature of the incident detection problem. The purpose of this study is to develop a new real-time incident detection model for urban diamond interchanges. The development of the algorithm is based on fuzzy logic. The incident detection model developed through this research is capable of detecting lane¬blocking incidents when their effects are manifested by certain patterns of deterioration in traffic conditions and, thereby, adjustments in signal control strategies are required. The model overcomes the boundary condition problem inherent in conventional threshold-based concepts. The model captures system-wide incident effects utilizing multiple measures for more accurate and reliable detection, and serves as a component module of a real-time traffic adaptive diamond interchange control system. The model is designed to be readily scalable and expandable for larger systems of arterial streets. The prototype incident detection model was applied to an actual diamond interchange to investigate its performance. A simulation study was performed to evaluate the model's performance in terms of detection rate, false alarm rate, and mean time to detect. The model's performance was encouraging, and the fuzzy logic based approach to incident detection is promising.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1345-1360
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• 2016

In this paper, we propose a new real-time human detection under omni-directional cameras for visual surveillance purpose, based on CNN with unified detection and AGMM. Compared to CNN-based state-of-the-art object detection methods. YOLO model-based object detection method boasts of very fast object detection, but with less accuracy. The proposed method adapts the unified detecting CNN of YOLO model so as to be intensified by the additional foreground contextual information obtained from pre-stage AGMM. Increased computational time incurred by additional AGMM processing is compensated by speed-up gain obtained from utilizing 2-D input data consisting of grey-level image data and foreground context information instead of 3-D color input data. Through various experiments, it is shown that the proposed method performs better with respect to accuracy and more robust to environment changes than YOLO model-based human detection method, but with the similar processing speeds to that of YOLO model-based one. Thus, it can be successfully employed for embedded surveillance application.

• 전기전자학회논문지
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• 제25권3호
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• pp.506-510
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• 2021

본 논문에서는 current detection 구조로 인하여 load regulation의 변화를 향상시킨 LDO를 제안하였다. 제안된 LDO 레귤레이터는 출력단에 제안된 current detection 회로를 추가하였다. 그로인하여 출력에 부하전류에 따른 전압 값의 regulation을 향상시켜 기존 LDO 레귤레이터보다 load Regulation의 변화량을 향상시켰다. 제안한 current detection 구조를 사용하여 부하전류의 변화에 따른 출력 변화를 약 60 % 가량 향상시킬 수 있었다. Cadence의 Virtuoso, Spectre 시뮬레이션을 사용하여 특성을 시뮬레이션 및 검증하였다.

• 대한임베디드공학회논문지
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• 제13권6호
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• pp.289-296
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• 2018

Drone detection in FMCW radar system needs complex techniques because a drone beat frequency is highly dynamic and unpredictable. Therefore, the current static signal processing algorithms cannot show appropriate detection accuracy. With dynamic signal fluctuation and environmental clutters, it can fail to detect a drone or make false detection. It affects to the radar system integrity and safety. Constant false alarm rate (CFAR), one of famous static signal process algorithm is effective for static environment. But for drone detection, it shows low detection accuracy. In this paper, we suggest neural network based FMCW radar system for detecting a drone. We use recurrent neural network (RNN) because it is the effective neural network for signal processing. In our FMCW radar system, one transmitter emits FMCW signal and four-way fixed receivers detect reflected drone beat frequency. The coordinate of the drone can be calculated with four receivers information by triangulation. Therefore, RNN only learns and inferences reflected drone beat frequency. It helps higher learning and detection accuracy. With several drone flight experiments, RNN shows false detection rate and detection accuracy as 21.1% and 96.4%, respectively.

• 반도체디스플레이기술학회지
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• 제17권1호
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• pp.54-61
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• 2018

In this study, two aspects were analyzed about the robot for removal of explosive devices. First, the cost analyses were performed to provide a reasonable solution for the acquirement of the system. It is processed by an engineering estimate method and the process was consisted of two ways : a system development expense and a mass production unit price. In additions, the resultant cost analyses were compared between the cases excluding and including a mines detection system. As results, in the case of the acquirement of the robot system for removal of explosive devices, it is recommended that the performance by improving the mines detection ability should be considered preferentially rather than the cost because the material cost for the mines detection system is negligible compared to the whole system cost. Second, as a way for improving the system performance by the mine detection function, the carbon nanotube (CNT) based sensor technology was studied in terms of sensitivity and simple productivity with presenting its preliminary experimental results. The detection electrodes were formed by a photolithography method using a photosensitive CNT paste. As results, this method was shown as a scalable and expandable technology for the excellent mines detection sensors.

• Ocean Systems Engineering
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• 제10권4호
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• pp.415-434
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• 2020

Detecting objects is important for the safe operation of ships, and enables collision avoidance, risk detection, and autonomous sailing. This study proposes a ship detection method from images and videos taken at sea using one of the state-of-the-art deep neural network-based object detection algorithms. A deep learning model is trained using a public maritime dataset, and results show it can detect all types of floating objects and classify them into ten specific classes that include a ship, speedboat, and buoy. The proposed deep learning model is compared to a universal trained model that detects and classifies objects into general classes, such as a person, dog, car, and boat, and results show that the proposed model outperforms the other in the detection of maritime objects. Different deep neural network structures are then compared to obtain the best detection performance. The proposed model also shows a real-time detection speed of approximately 30 frames per second. Hence, it is expected that the proposed model can be used to detect maritime objects and reduce risks while at sea.

• 정보보호학회논문지
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• 제13권2호
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• pp.99-106
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• 2003

네트워크 환경에서의 다양한 침입은 심각한 위험을 초래 할 수 있기 때문에 침입을 효과적으로 탐지하기 위해 데이터마이닝 기법을 발전시켜 왔다. 비정상행위 탐지 기술은 순수 데이터로 학습한 후, 비정상행위를 탐지하기 때문에 정교한 정상행위 패턴 생성이 필수적이다. 순수한 학습 데이터의 생성은 시간과 비용이 많이 드는 단점이 있다. 따라서 네트워크 상의 데이터에 대한 특징을 파악하는 것이 중요하다. 본 논문에서는 데이터마이닝의 연관규칙 및 클러스터링기법을 비정상행위 탐지에 적용하였고, 패킷내의 판정 요소에 정보이론 척도를 적용하여 불필요한 데이터를 필터링하는 방법을 제시하였다. 또한 가변길이 트랜잭션을 네트워크상의 분석 단위를 정의하는 기준으로 제시하여 행위 패턴 생성에 보다 묘사성이 높음을 보였다.

• 정보보호학회논문지
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• 제12권4호
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• pp.29-39
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• 2002

침입행위에 대한 비정상행위 탐지방법은 탐지에 대한 오판율이 높게 나타난다. 즉, 실제 침입이 아닌데 침입으로 판정하는 과탐지와 실제 침입인데 탐지하지 못하는 미탐지에 대한 오판율의 경우이다. 침입탐지의 민감도를 향상시키기 위하여 오용행위 및 비정상행위 탐지센서들 사이의 관련성을 이용하여 오판율을 감소하는 통합탐지의 방법을 연구하였다. 정상행위 및 비정상행위에 대해 하나의 탐지센서로부터의 결과가 다른 탐지센서에 의한 결과와 어떠한 관련성을 갖고 있는지의 반영비율을 오프라인에서 생성하고, 이를 실시간에 탐지된 결과에 적용하여 오판율을 감소하도록 하였다.