• 한국가시화정보학회:학술대회논문집
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• 2006.12a
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• pp.102-108
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• 2006

The three-dimensional flows in the Weis-Fogh mechanism are studied by flow visualization and numerical simulation by the discrete vortex method. In this mechanism, two wings open, touching their trailing edges (fling), and rotate in opposite directions in the horizontal plane. The structure of the vortex systems shed from the wings is very complicated and their effects on the forces on the wings have not yet been clarified. The discrete vortex method, especially the vortex stick method, is employed to investigate the vortex structure in the wake of the two wings. The wings are represented by lattice vortices, and the shed vortices are expressed by discrete three-dimensional vortex sticks. The vortex distributions and the velocity field are calculated. The pressure is estimated by the Bernoulli equation, and the lift on the wing are also obtained.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.7
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• pp.771-779
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• 2004

Unsteady behavior of the early wake in the viscous flow field past an impulsively started semicircular cylinder is studied numerically. In this paper, we propose the hybrid diffusion scheme to simulate dynamic characteristics of wake such as a fishtail-like flapping and an alternate vortex-shedding more accurately. This diffusion scheme based on particle strength exchange is mixed with the stochastic nature of random walk method. Also, the viscous splitting algorithm which calculates convective and diffusion terms successively is applied in order to handle random walk method effectively. Consequently, the early behavior of wake due to the breakdown of symmetrical vortici balance is more practically simulated with the vortex particle method.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.16-21
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• 2010

잠재 의미를 색인하는 것은 문서 색인에 있어서 그 색인 품질에 주요한 요인을 차지한다. 본 논문에서 살펴보는 의미 확산 방법은 문서 집합에 나타나는 단어들의 의미적 연관성을 바탕으로 활성화 작용 모델(spreading activation model)을 구축하고 색인 대상 문서의 단어 분포를 출발점으로 삼아, 그 모델 안에서 의미적으로 수렴할 수 있도록 랜덤 워크 방법(random walk method)1)을 수정한 변형 방법을 이용해 확률을 확산시킨다. 이 방법은 단어 사이의 연관성을 따라 탐색하며 동의어와 다의어 등 단순 단어 일치로는 알 수 없는 의미적 유사 단어들이 의미 있는 확률 분포를 갖게 한다. 이는 단어들의 의미 분포를 가중치 그래프를 통해 보다 합리적으로 다루게 된다. 실험에서는 문서 분류를 시행하여 평균 정확도 및 정확도-재현율 곡선을 산출하였고, 비교 실험을 통해서 전반적인 우수성을 관찰할 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.127-130
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• 2020

최근 변화하는 소비패턴으로 인해 당일 및 새벽 배송 등의 서비스가 보편화되고 있다. 해당 서비스는 배송 정보를 입력 시 건물에 자유롭게 출입할 수 있는 공동 현관 비밀번호를 기입해야 한다. 이는 이미 빈번하게 발생하고 있는 무단 주거 침입 등의 범죄에 더 쉽게 노출되도록 할 수 있는 위험 요소이다. 본 논문에서는 신뢰할 수 있는 사용자만이 참여 가능한 프라이빗 블록체인 네트워크에서의 차량 번호판 인식 및 스마트 컨트랙트를 통해 랜덤한 마스터 비밀번호를 제공하여, 보안적으로 취약한 비밀번호 기입 절차를 없애고 검증 받은 사용자에게만 출입을 허가하는 방식을 제안한다.

• Journal of the Korea Convergence Society
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• v.11 no.4
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• pp.33-39
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• 2020

Interest in 5G communication security has been growing recently amid growing expectations for 5G technology with faster speed and stability than LTE. However, 5G has so far included disparate areas, so it has not yet fully supported the issues of security. This paper proposes a blockchain-based IoT management model in order to efficiently provide the authentication of users using IoT in 5G In order to efficiently fuse the authentication of IoT users with probabilistic theory and physical structure, the proposed model uses two random keys in reverse direction at different layers so that two-way authentication is achieved by the managers of layers and layers. The proposed model applied blockchain between grouped IoT devices by assigning weights to layer information of IoT information after certification of IoT users in 5G environment is stratified on a probabilistic basis. In particular, the proposed model has better functions than the existing blockchain because it divides the IoT network into layered, multi-layered networks.

• Journal of Convergence for Information Technology
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• v.10 no.6
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• pp.1-7
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• 2020

The demand on the underwater communications is extremely increasing in searching for underwater resources, marine expedition, or environmental researches, yet there are many problems with the wireless communications because of the characteristics of the underwater environments. Especially, with the underwater wireless networks, there happen inevitable delay time and spacial inequality due to the distances between the nodes. To solve these problems, this paper suggests a new solution based on ALOHA-Q. The suggested method use random NAV value. and Environments take reward through communications success or fail. After then, The environments setting NAV value from reward. This model minimizes usage of energy and computing resources under the underwater wireless networks, and learns and setting NAV values through intense learning. The results of the simulations show that NAV values can be environmentally adopted and select best value to the circumstances, so the problems which are unnecessary delay times and spacial inequality can be solved. Result of simulations, NAV time decreasing 17.5% compared with original NAV.

• Journal of Korea Multimedia Society
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• v.11 no.8
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• pp.1072-1081
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• 2008

We can categorize the network address allocation mechanism into two types. One is to assign a unique network address using the address allocation server and the other is to make a random address by itself and process the DAD(Duplicate Address Detection) procedure. In this paper we suggest a new address allocation mechanism based on the signal strength for the PLC-based home network. As the combined mechanism of two types this mechanism allocates a unique address for the new node from one of the existing nodes with the simple equation and with the priority based on the signal strength from the new node to the existing nodes. We can use this mechanism for the self-healing function when the packet from the source node may not be delivered to the destination node directly. We developed the simulator for our mechanism using the C# programming and verified the network address assigned uniquely based on the signal strength.

• Journal of KIISE
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• v.44 no.11
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• pp.1231-1235
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• 2017

Given the network structure in online social network, it is important to determine a way to distinguish spam accounts from the network features. In online social network, the service provider attempts to detect social spamming to maintain their service quality. However the spammer group changes their strategies to avoid being detected. Even though the spammer attempts to act as legitimate users, certain distinguishable structural features are not easily changed. In this paper, we investigate a way to generate meaningful network structure features, and suggest spammer detection method using recursive structural features. From a result of real-world dataset experiment, we found that the proposed algorithm could improve the classification performance by about 8%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.5
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• pp.581-588
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• 2019

In this paper, I propose an modified back-off algorithm to improve the fairness for slotted ALOHA sensor networks. In hierarchical networks, the performance degradation of a specific node can cause degradation of the overall network performance in case the data transmitted by lower nodes is needed to be synthesized and processed by an upper node. Therefore it is important to ensure the fairness of transmission performance to all nodes. The proposed scheme choose a back-off time of a node considering the previous transmission results as well as the current transmission result. Moreover a node that failed to transmit consecutively is given gradually shorter back-off time but a node that is success to transmit consecutively is given gradually longer back-off time. Through simulations, I compare and analyze the performance of the proposed scheme with the binary exponential back-off algorithm(BEB). The results show that the proposed scheme reduces the throughput slightly compared to BEB but improves the fairness significantly.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.157-167
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• 2020

With the recent advancement of computer hardware and the contribution of open source libraries to facilitate access to artificial intelligence technology, the use of machine learning (ML) and deep learning (DL) technologies in various fields of exploration geophysics has increased. In addition, ML researchers have developed complex algorithms to improve the inference accuracy of various tasks such as image, video, voice, and natural language processing, and now they are expanding their interests into the field of automatic machine learning (AutoML). AutoML can be divided into three areas: feature engineering, architecture search, and hyperparameter search. Among them, this paper focuses on hyperparamter search with Bayesian optimization, and applies it to the problem of facies classification using seismic data and well logs. The effectiveness of the Bayesian optimization technique has been demonstrated using Vincent field data by comparing with the results of the random search technique.