• 한국군사과학기술학회지
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• 제13권5호
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• pp.824-830
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• 2010

It is not easy job to find a underwater target using sonar system in the ASW operations. Many researchers have tried to solve anti-submarine search problem aiming to maximize the probability of detection under limited searching conditions. The classical 'Search Theory' deals with search allocation problem and search path problem. In both problems, the main issue is to prioritize the searching cells in a searching area. The number of possible searching path that is combination of the consecutive searching cells increases rapidly by exponential function in the case that the number of searching cells or searchers increases. The more searching path we consider, the longer time we calculate. In this study, an effective algorithm that can maximize the probability of detection in shorter computation time is presented. We show the presented algorithm is quicker method than previous algorithms to solve search problem through the comparison of the CPU computation time.

• 스마트미디어저널
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• 제6권3호
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• pp.49-56
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• 2017

Images and Videos that include the human face contain a lot of information. Therefore, accurately extracting human face is a very important issue in the field of computer vision. However, in real life, human faces have various shapes and textures. To adapt to these variations, A model-based approach is one of the best ways in which unknown data can be represented by the model in which it is built. However, the model-based approach has its weaknesses when the motion between two frames is big, it can be either a sudden change of pose or moving with fast speed. In this paper, we propose an enhanced human face-tracking model. This approach included human face detection and motion estimation using Cascaded Convolutional Neural Networks, and continuous human face tracking and modeling correction steps using the Active Appearance Model. A proposed system detects human face in the first input frame and initializes the models. On later frames, Cascaded CNN face detection is used to estimate the target motion such as location or pose before applying the old model and fit new target.

• Journal of Information Processing Systems
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• 제14권1호
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• pp.56-78
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• 2018

Security issue in mobile ad hoc network (MANET) is a promising research. In 2011, we had accomplished a survey of black hole attacks in MANETs. However network technology is changing with each passing day, a vast number of novel schemes and papers have been proposed and published in recent years. In this paper, we survey the literature on malicious attacks in MANETs published during past 5 years, especially the black hole attack. Black hole attacks are classified into non-cooperative and collaborative black hole attacks. Except black hole attacks, other attacks in MANET are also studied, e.g., wormhole and flooding attacks. In addition, we conceive the open issues and future trends of black hole detection and prevention in MANETs based on the survey results of this paper. We summarize these detection schemes with three systematic comparison tables of non-cooperative black hole, collaborative black hole and other attacks, respectively, for a comprehensive survey of attacks in MANETs.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2018년도 추계학술발표대회
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• pp.1001-1004
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• 2018

많은 양의 데이터가 수집되는 산업분야에서의 IoT 플렛폼 활용도가 높아지면서 IoT플랫폼의 성능과 이상 감지가 중요한 요소가 되고 있다. 본 논문에서는 IoT 플랫폼의 데이터 수집 성능을 저해하지 않으면서 산업분야에 활용되는 디바이스의 이상을 감지하는 시스템을 제안한다. 제안한 시스템은 Soft Real-time 서비스를 제공하기 위해 데이터 전송주기를 고려한 Micro Batch를 활용했으며, 실험에는 산업분야의 이상 상황에 대한 자료수집이 사전에 이루어지기 어려운 상황을 고려해 Hotelling's $T^2$를 활용한 분석모델을 적용하였고 Hotelling's $T^2$는 이상징후를 사전에 감지하였다.

• 한국산학기술학회논문지
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• 제11권11호
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• pp.4543-4552
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• 2010

리더는 그룹 내의 여러 프로세스 중에서 조정자 역할을 하는 특별한 프로세스이다. 분산 시스템에서 하나의 조정자를 선출하는 과정을 리더선출이라 하며 이는 결함허용 분산시스템을 구축하기 위한 매우 중요한 문제이다. 현재 정상 작동하고 있는 두 개의 모바일 ad hoc 네트워크 그룹들이 병합되면 두 개의 리더가 존재하게 된다. 이것은 안전성 속성에 위배되므로 이를 감지하고 처리할 수 있는 메커니즘이 필요하다. 본 논문에서는 모바일 ad hoc 분산 컴퓨팅 시스템에서 그룹 멤버십 탐지 기반의 리더 경쟁 프로토콜을 제안하고 이를 시제논리로 검증하고자 한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권2호
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• pp.118-123
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• 2011

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.89-100
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• 2021

Smart Grid Network (SGN) is a next generation electrical power network which digitizes the power distribution grid and achieves smart, efficient, safe and secure operations of the electricity. The backbone of the SGN is information communication technology that enables the SGN to get full control of network station monitoring and analysis. In any network where communication is involved security is essential. It has been observed from several recent incidents that an adversary causes an interruption to the operation of the networks which lead to the electricity theft. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we have introduced a machine learning based SVM method that detects malicious nodes in a smart grid network. The algorithm collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) classifies nodes into Normal or malicious nodes giving the statues of 1 for normal nodes and status of -1 for malicious -abnormal-nodes. Once the malicious nodes have been detected, we have done a trust evaluation based on the nodes history and recorded data. In the simulation, we have observed that our detection rate is almost 98% where the false alarm rate is only 2%. Moreover, a Trust value of 50 was achieved. As a future work, countermeasures based on the trust value will be developed to solve the problem remotely.

• 한국시뮬레이션학회논문지
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• 제19권4호
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• pp.151-159
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• 2010

인터넷은 이제 일상생활에서 떼어놓을 수 없는 생활의 일부가 되었다. 그러나 인터넷은 애초에 보안의 개념 없이 만들어졌기 때문에 악의적인 사용자가 네트워크를 통해 시스템에 침투하여 시스템을 마비시키거나 개인정보를 탈취하는 문제들이 커다란 사회적 이슈가 되고 있다. 또한 최근 평범한 일반 사람들도 네트워크 공격 툴 사용으로 인한 DoS 공격이 가능해짐에 따라 인터넷 환경에서 큰 위협을 주고 있다. 그러므로 효율적이고 강력한 공격 탐지 시스템이 인터넷 환경에서 매우 중요하게 되었다. 그러나 이러한 공격을 완벽하게 막아내는 것은 매우 어려운 일이다. 본 논문에서는 DoS 공격의 탐지를 위한 알고리즘을 제안하고, 이를 이용한 공격탐지도구를 제시한다. 먼저 정상상태에서의 학습단계를 거쳐서, 학습된 임계치 허용량, 각 포트로 유입되는 패킷의 개수, 중간값 그리고 각 포트별 평균사용률을 계산하고, 이 값을 바탕으로 공격탐지가 이루어지는 3단계 판별 방법을 제안하였다. 제안한 방법에 맞는 공격 탐지 도구를 제작하여 실험을 하였으며, 그 결과 각 포트별 평균사용률과 단위 시간당 패킷량 중간값의 차이와 학습된 임계치 허용량의 비교는 공격 탐지에 효율적임을 알 수 있다. 또한 네트워크 데이터를 들여다 볼 필요 없이, 패킷의 개수만을 이용하여 공격을 탐지함으로써 간단히 구현할 수 있음을 알 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권2호
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• pp.800-816
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• 2018

In recent years, video surveillance research has been able to recognize various behaviors of pedestrians and analyze the overall situation of objects by combining image analysis technology and deep learning method. Human Activity Recognition (HAR), which is important issue in video surveillance research, is a field to detect abnormal behavior of pedestrians in CCTV environment. In order to recognize human behavior, it is necessary to detect the human in the image and to estimate the pose from the detected human. In this paper, we propose a novel approach for 2D Human Pose Estimation based on object detection using RGB-D information. By adding depth information to the RGB information that has some limitation in detecting object due to lack of topological information, we can improve the detecting accuracy. Subsequently, the rescaled region of the detected object is applied to ConVol.utional Pose Machines (CPM) which is a sequential prediction structure based on ConVol.utional Neural Network. We utilize CPM to generate belief maps to predict the positions of keypoint representing human body parts and to estimate human pose by detecting 14 key body points. From the experimental results, we can prove that the proposed method detects target objects robustly in occlusion. It is also possible to perform 2D human pose estimation by providing an accurately detected region as an input of the CPM. As for the future work, we will estimate the 3D human pose by mapping the 2D coordinate information on the body part onto the 3D space. Consequently, we can provide useful human behavior information in the research of HAR.

• 대한원격탐사학회지
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• 제27권6호
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• pp.703-715
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• 2011

Forests have been considered one of the most important ecosystems on the earth, affecting the lives and environment. The sustainable forest management requires accurate and timely information of forest and tree parameters. Appropriately interpreted remotely sensed imagery can provide quantitative data for deriving forest information temporally and spatially. Especially, analysis of individual tree detection and crown delineation is significant issue, because individual trees are basic units for forest management. Individual trees in aerial imagery have reflectance characteristics according to tree species, crown shape and hierarchical status. This study suggested a method that identified individual trees and delineated crown boundaries through adopting gradient method algorithm to amplified greenness data using red and green band of aerial imagery. The amplification of specific band value improved possibility of detecting individual trees, and gradient method algorithm was performed to apply to identify individual tree tops. Additionally, tree crown boundaries were explored using spectral intensity pattern created by geometric characteristic of tree crown shape. Finally, accuracy of result derived from this method was evaluated by comparing with the reference data about individual tree location, number and crown boundary acquired by visual interpretation. The accuracy ($\hat{K}$) of suggested method to identify individual trees was 0.89 and adequate window size for delineating crown boundaries was $19{\times}19$ window size (maximum crown size: 9.4m) with accuracy ($\hat{K}$) at 0.80.