• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5238-5244
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• 2011

Recently, wireless sensor networks have become increasingly interesting areas over extensive application fields such as military, ecological, and health-related areas. Almost sensor networks have mission-critical tasks that requires very high security. Therefore, extensive work has been done for securing sensor networks from outside attackers, efficient cryptographic systems, secure key management and authorization, but little work has yet been done to protect these networks from inside threats. This paper proposed an method to select which nodes should activate their idle nodes as detectors to be able to watch all packets in the sensor network. Suggested method is modeled as optimization equation, and heuristic Greedy algorithm based simulation results are presented to verify my approach.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1676-1687
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• 2008

In this paper, we proposed a new multi-view human recognition method based on face and gait features detection algorithm. For getting the position of moving object, we used the different of two consecutive frames. And then, base on the extracted object, the first important characteristic, walking direction, will be determined by using the contour of head and shoulder region. If this individual appears in camera with frontal direction, we will use the face features for recognition. The face detection technique is based on the combination of skin color and Haar-like feature whereas eigen-images and PCA are used in the recognition stage. In the other case, if the walking direction is frontal view, gait features will be used. To evaluate the effect of this proposed and compare with another method, we also present some simulation results which are performed in indoor and outdoor environment. Experimental result shows that the proposed algorithm has better recognition efficiency than the conventional sing]e view recognition method.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.769-781
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• 2017

Deep learning networks like Convolutional Neural Networks (CNNs) show successful performances in many computer vision applications such as image classification, object detection, and so on. For implementation of deep learning networks in embedded system with limited processing power and memory, deep learning network may need to be simplified. However, simplified deep learning network cannot learn every possible scene. One realistic strategy for embedded deep learning network is to construct a simplified deep learning network model optimized for the scene images of the installation place. Then, automatic training will be necessitated for commercialization. In this paper, as an intermediate step toward automatic training under fisheye camera environments, we study more precise human localization in fisheye images, and propose an accurate human localization method, Automatic Ground-Truth Labelling Method (AGTLM). AGTLM first localizes candidate human object bounding boxes by utilizing GoogLeNet-LSTM approach, and after reassurance process by GoogLeNet-based CNN network, finally refines them more correctly and precisely(tightly) by applying saliency object detection technique. The performance improvement of the proposed human localization method, AGTLM with respect to accuracy and tightness is shown through several experiments.

• Annual Conference of KIPS
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• 2015.04a
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• pp.424-427
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• 2015

근래 들어 모바일 기기의 시스템을 장악하여 사용자의 기밀 정보를 빼내는 악성 행위의 한 방법으로 Code Reuse Attack (CRA)이 널리 사용되고 있다. 이와 같은 CRA를 막기 위하여 call-return이 일어날 때마다 이들 address를 비교해 보는 shadow stack과 branch에 대한 몇 가지 규칙을 두어 CRA 를 탐지하는 branch regulation과 같은 방식이 연구되었다. 우리는 shadow stack과 branch regulation을 종합하여 여러 종류의 CRA를 적은 성능 오버헤드로 탐지할 수 있는 CRA Detection System을 만들고자 한다. 이를 위하여 반드시 선행 되어야 할 연구인 바이너리 파일 분석과 meta-data 생성 및 압축 기술을 제안한다. 실험 결과 생성된 meta-data는 압축 기술을 적용하기 전보다 1/2에서 1/3 가량으로 그 크기가 줄어들었으며 CRA Detection System의 탐지가 정상적으로 동작하는 것 또한 확인할 수 있었다.

• The Korean Journal of Legal Medicine
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• v.41 no.2
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• pp.41-45
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• 2017

Fetal DNA (fDNA) detection in maternal serum is a challenge due to low copy number and the smaller size of fDNA fragments compared to DNA fragments derived from the mother. Massively parallel sequencing (MPS) is a useful technique for fetal genetic analysis that is able to detect and quantify small amounts of DNA. In this study, seven clinical samples of maternal serum potentially containing fDNA were analyzed with a commercial single nucleotide polymorphism (SNP) panel, the HID-Ion $AmpliSeq^{TM}$ Identity Panel, and the results were compared to those from previous studies. Reference profiles for mothers and fetuses were not available, but multiple Y chromosomal SNPs were detected in two samples, indicating that fDNA was present in the serum and thereby validating observations of autosomal SNPs. This suggests that SNP-based MPS can be valuable for fDNA detection, thereby offering an insight into fetal genetic status. This technology could also be used to detect small amounts of DNA in mixed DNA samples for forensic applications.

• International Journal of Reliability and Applications
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• v.4 no.1
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• pp.1-12
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• 2003

Many software reliability growth models (SRGM's) based on a nonhomogeneous Poisson process (NHPP) have been proposed by many researchers. Most of the SRGM's which have been proposed up to the present treat the event of software fault-detection in the testing and operational phases as a counting process. However, if the size of the software system is large, the number of software faults detected during the testing phase becomes large, and the change of the number of faults which are detected and removed through debugging activities becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. Therefore, in such a situation, we can model the software fault-detection process as a stochastic process with a continuous state space. In this paper, we propose a new software reliability growth model describing the fault-detection process by applying a mathematical technique of stochastic differential equations of an Ito type. We also compare our model with the existing SRGM's in terms of goodness-of-fit for actual data sets.

• Computers and Concrete
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• v.21 no.6
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• pp.727-739
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• 2018

Crack is the most common typical feature of concrete deterioration, so routine monitoring and health assessment become essential for identifying failures and to set up an appropriate rehabilitation strategy in order to extend the service life of concrete structures. At present, image segmentation algorithms have been applied to crack analysis based on inspection images of concrete structures. The results of crack segmentation offering crack information, including length, width, and area is helpful to assist inspectors in surface inspection of concrete structures. This study proposed an algorithm of image segmentation enhancement, named morphological segmentation based on edge detection-II (MSED-II), to concrete crack segmentation. Several concrete pavement and building surfaces were imaged as the study materials. In addition, morphological operations followed by cross-curvature evaluation (CCE), an image segmentation technique of linear patterns, were also tested to evaluate their performance in concrete crack segmentation. The result indicates that MSED-II compared to CCE can lead to better quality of concrete crack segmentation. The least area, length, and width measurement errors of the concrete cracks are 5.68%, 0.23%, and 0.00%, respectively, that proves MSED-II effective for automatic measurement of concrete cracks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.2
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• pp.321-330
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• 2019

Prevention of corporate confidentiality leakage by insiders in enterprises is an essential task for the survival of enterprises. In order to prevent information leakage by insiders, companies have adopted security solutions, but there is a limit to effectively detect abnormal behavior of insiders with access privileges. In this study, we use the Unsupervised Learning algorithm of the machine learning technique to effectively and efficiently cluster the normal and abnormal access logs of the worker's work screen in the manufacturing information system, which includes the company's product manufacturing history and quality information. We propose an optimal feature selection model for anomaly detection by studying clustering methods.

• Structural Engineering and Mechanics
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• v.68 no.6
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• pp.735-745
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• 2018

The Big Bang-Big Crunch (BB-BC) algorithm is an effective global optimization technique of swarm intelligence with drawbacks of being easily trapped in local optimal results and of converging slowly. To overcome these shortages, an improved BB-BC algorithm (IBB-BC) is proposed in this paper with taking some measures, such as altering the reduced form of exploding radius and generating multiple mass centers. The accuracy and efficiency of IBB-BC is examined by different types of benchmark test functions. The IBB-BC is utilized for damage detection of a simply supported beam and the European Space Agency structure with an objective function established by structural frequency and modal data. Two damage scenarios are considered: damage only existed in stiffness and damage existed in both stiffness and mass. IBB-BC is also validated by an existing experimental study. Results demonstrated that IBB-BC is not trapped into local optimal results and is able to detect structural damages precisely even under measurement noise.

• Journal of IKEEE
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• v.23 no.1
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• pp.266-272
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• 2019

Multicopters have become more popular since they are advantageous in their ability to take off and land vertically. In order to guarantee the normal operations of such multicopters, the problem of fault detection and isolation is very important. In this paper, a new method for detecting and isolating an actuator fault of a hexacopter is proposed based on the analytical approach. The residual is newly defined using the angular velocities of actuators estimated by the mathematical model and an actuator fault is detected comparing the residuals to a threshold. And a fault is isolated combining a dynamic model and generated residuals when a fault is detected. The proposed method is a simple, but effective technique because it is based on mathematical model. The results of the computer simulation are also given to demonstrate the validity of the proposed algorithm in case of a single failure.