• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.100-103
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• 2021

IoT 와 인공지능을 접하려는 시도는 최근 들어서 많은 발전을 보이고 있다. 본 논문은 컴퓨팅 파워가 제한되는 작은 디바이스 IoT 의 한계를 극복하기 위하여 ROS 를 이용하여 복잡한 연산을 무선 통신으로 오프로딩하는 기법을 제안한다. 제안된 자율주행카드 시스템은 카트 이용 고객 개개인을 검출하고 추적하되 컴퓨터 비전 알고리즘과 LiDAR 센서를 이용하며, 음성인식 알고리즘을 적용하여 기계와 인간의 감성공학적 소통이 가능한 융합형 자율주행카트를 구현한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.52-55
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• 2022

5G 시대에 스마트 모바일 기기가 기하급수적으로 증가하면서 멀티 액세스 엣지 컴퓨팅(MEC)이 유망한 기술로 부상했다. 낮은 지연시간 안에 계산 집약적인 서비스를 제공하기 위해 MEC 서버로 오프로딩하는 특히, 태스크 도착률과 무선 채널의 상태가 확률적인 MEC 시스템 환경에서의 오프로딩 연구가 주목받고 있다. 본 논문에서는 차량의 전력과 지연시간을 최소화하기 위해 로컬 실행을 위한 연산 자원과 오프로딩을 위한 전송 전력을 할당하는 심층 강화학습 기반의 오프로딩 기법을 제안하였다. Deep Deterministic Policy Gradient (DDPG) 기반 기법과 Deep Q-network (DQN) 기반 기법을 차량의 전력 소비량과 큐잉 지연시간 측면에서 성능을 비교 분석하였다.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1035-1038
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• 2005

This paper describes the designs and the implementations of two H/W IPSec Systems, look-aside and inline, on TOE (Transport Offloading Engine). These systems aim for guaranteeing the security of datagram networks while preserving the bandwidth of gigabit networks. The TOE offloads a host CPU from network burdens, so that it makes the gigabit wire speed possible, and then deeper level security architecture of the IPSec guarantees the security of gigabit service network dominated by datagram packets. The focus of this paper is to minimize the TOE's performance degradation caused by the computation-oriented IPSec. The look-aside IPSec system provides a significant improvement in the CPU offload of the IPSec cryptography loads. However, the inline system completely offloads the host CPU from whole IPSec loads, providing significant additional cost saving compared to the look-aside system. In this paper, the implementations of TOE cards including commercial IPSec processors are presented. As the result of performance evaluation with the protocol analyzer, we can get the fact that the inline IPSec system is 8 times faster than the S/W system and 2 times faster than the look-aside system.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.491-500
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• 2022

It has been noted that an accurate estimation of wind loads on offshore structures such as an FLNG (Liquefied Natural Gas Floating P roduction Storage Offloading Units, LNG FPSOs) with a large topside plays an important role in the safety design of hull and mooring system. Therefore, the present study aims to develop a computational model for estimating the wind load acting on an FLNG. In particular, it is the sequel to the previous research by the author. The numerical computation model in the present study was modified based on the previous research. Numerical analysis for estimating wind loads was performed in two conditions for an interval of wind direction (α), 15° over the range of 0° to 360°. One condition is uniform wind speed and the other is the NPD model reflecting the wind speed profile. At first, the effect of sand-grain roughness on the speed profile of the NPD model was studied. Based on the developed NPD model, mesh convergence tests were carried out for 3 wind headings, i.e. head, quartering, and beam. Finally, wind loads on 6-degrees of freedom were numerically estimated and compared by two boundary conditions, uniform speed, and the NPD model. In the present study, a commercial RANS-based viscous solver, STAR-CCM+ (ver. 17.02) was adopted. In summary, wind loads in surge and yaw from the wind speed profile boundary condition were increased by 20.35% and 34.27% at most. Particularly, the interval mean of sway (45° < α <135°, 225° < α < 315°) and roll (60° < α < 135°, 225° < α < 270°) increased by 15.60% and 10.89% against the uniform wind speed (10m/s) boundary condition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.232-235
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• 2015

In recent years, mobile devices are equipped with functionalities comparable to those computers. However, mobile devices have limited resources due to constraints, such as low processing power, limited memory, unpredictable connectivity, and limited battery life. To enhance the capacity of mobile devices, an interesting idea is to use cloud computing and virtualization techniques to shift the workload from mobile devices to a computational infrastructure. Those techniques consist of migrating resource-intensive computations from a mobile device to the resource-rich cloud, or server (called nearby infrastructure). In order to achieve their goals, researchers designed mobile cloud applications models (examples: CloneCloud, Cloudlet, and Weblet). In this paper, we want to highlight on cloudlet architecture (nearby infrastructure with mobile device), its methodology and discuss about the impact of distance between cloudlet and mobile device in our work design.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2671-2676
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• 2015

Nowadays, Mobile devices are now capable of supporting a wide range of applications. Unfortunately, some of applications demand an ever increasing computational power and mobile devices have limited resources due to their constraints, such as low processing power, limited memory, unpredictable connectivity, and limited battery life. To deal with mobile devices' constraints, researchers envision extending cloud computing services to mobile devices using virtualization techniques to shift the workload from mobile devices to a powerful computational infrastructure. Those techniques consist of migrating resource-intensive computations from a mobile device to the resource-rich cloud, or server (called nearby infrastructure). In this paper, we want to highlight on cloudlet architecture (nearby infrastructure with mobile devices), its functioning and in our future work, analyze effects of distance between cloudlet and mobile devices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.786-792
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• 2022

In the cloud computing environment, there has been a lot of research into the Fog/Edge Computing (FEC) paradigm for securing user proximity of application services and computation offloading to alleviate service delay difficulties. The method of predicting dynamic location change patterns of edge devices (moving objects) requesting application services is critical in this FEC environment for efficient computing resource distribution and deployment. This paper proposes an optimal moving pattern extraction algorithm in which variable weights (distance, time, congestion) are applied to selected paths in addition to a support factor threshold for frequency patterns (moving objects) of edge devices. The proposed algorithm is compared to the OPE_freq [8] algorithm, which just applies frequency, as well as the A^* and Dijkstra algorithms, and it can be shown that the execution time and number of nodes accessed are reduced, and a more accurate path is extracted through experiments.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.305-316
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• 2022

With the flood of digital data owing to the Internet of Things and big data, cloud service providers that process and store vast amount of data from multiple users can apply duplicate data elimination technique for efficient data management. The user experience can be improved as the notion of edge computing paradigm is introduced as an extension of the cloud computing to improve problems such as network congestion to a central cloud server and reduced computational efficiency. However, the addition of a new edge device that is not entirely reliable in the edge computing may cause increase in the computational complexity for additional cryptographic operations to preserve data privacy in duplicate identification and elimination process. In this paper, we propose an efficiency-improved duplicate data elimination protocol while preserving data privacy with an optimized user-edge-cloud communication framework by utilizing a trusted execution environment. Direct sharing of secret information between the user and the central cloud server can minimize the computational complexity in edge devices and enables the use of efficient encryption algorithms at the side of cloud service providers. Users also improve the user experience by offloading data to edge devices, enabling duplicate elimination and independent activity. Through experiments, efficiency of the proposed scheme has been analyzed such as up to 78x improvements in computation during data outsourcing process compared to the previous study which does not exploit trusted execution environment in edge computing architecture.