• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4572-4586
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• 2019

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3043-3067
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• 2022

With the mobile traffic in the network increases exponentially, multi-access edge computing (MEC) develops rapidly. MEC servers are deployed geo-distribution, which serve many mobile terminals locally to improve users' QoE (Quality of Experience). When the cache space of a MEC server is full, how to replace the cached videos is an important problem. The problem is also called the cache replacement problem, which becomes more complex due to the dynamic video popularity and the varied video sizes. Therefore, we proposed a new cache replacement scheme based on local video popularity and video size to solve the cache replacement problem of MEC servers. First, we built a local video popularity model, which is composed of a popularity rise model and a popularity attenuation model. Furthermore, the popularity attenuation model incorporates a frequency-dependent attenuation model and a frequency-independent attenuation model. Second, we formulated a utility based on local video popularity and video size. Moreover, the weights of local video popularity and video size were quantitatively analyzed by using the information entropy. Finally, we conducted extensive simulation experiments based on the proposed scheme and some compared schemes. The simulation results showed that our proposed scheme performs better than the compared schemes in terms of hit rate, average delay, and server load under different network configurations.

• Journal of the Korea Organic Resources Recycling Association
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• v.27 no.3
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• pp.41-48
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• 2019

Mixed expeller cake has been one of soil management to improve crop productivity and soil fertility. But, there was a little information on optimum mixed expeller cake application for chinese cabbage. So, in this study, we were evaluated the preplant optimum application rate of mixed expeller cake(MEC) in chinese cabbage cultivation at field. Treatments consist of control, inorganic fertilizer($N-P_2O_5-K_2O$ : $320-78-198kg\;ha^{-1}$), MEC(50, 100, 150% on preplant application standard $110kg\;ha^{-1}$ as N, topdressing : $210kg\;ha^{-1}$ as N). In results, growth characteristics was not significantly different. But, yield was increased when application rate was increased. And MEC 150% treatment showed similar yield as inorganic treatment. There was no significant difference in soil pH, OM, $Av.P_2O_5$, $NH_4-N$ and Ex.K. But, soil EC and $NO_3-N$ were increased when MEC level increased. As a results, MEC 150% can be proposed as preplant optimum application rate of MEC from this study. But abuse of MEC and long-term using caused about salt accumulation in soil.

• Journal of Communications and Networks
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• v.18 no.2
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• pp.210-217
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• 2016

In this paper, we propose a low complexity multiple-input multiple-output (MIMO) detection algorithm with adaptive interference mitigation in downlink multiuser MIMO (DL MU-MIMO) systems with quantization error of the channel state information (CSI) feedback. In DL MU-MIMO systems using the imperfect precoding matrix caused by quantization error of the CSI feedback, the station receives the desired signal as well as the residual interference signal. Therefore, a complexMIMO detection algorithm with interference mitigation is required for mitigating the residual interference. To reduce the computational complexity, we propose a MIMO detection algorithm with adaptive interference mitigation. The proposed algorithm adaptively mitigates the residual interference by using the maximum likelihood detection (MLD) error criterion (MEC). We derive a theoretical MEC by using the MLD error condition and a practical MEC by approximating the theoretical MEC. In conclusion, the proposed algorithm adaptively performs interference mitigation when satisfying the practical MEC. Simulation results show that the proposed algorithm reduces the computational complexity and has the same performance, compared to the generalized sphere decoder, which always performs interference mitigation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.3
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• pp.423-431
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• 1997

A new decision tree generator MEC is proposed in this paper, which uses the difference of multi-base entropy as a consistent criterion for discretization and selection of attributes. To evaluate the performance of the proposed generator, it is compared to other generators which use criteria based on entropy and adopt different discretization styles. As an experimental result, it is shown that the proposed generator produces the most efficient classifiers, which have the least number of leaves at the same error rate, regardless of whether attribute values constituting the training set are discrete or continuous.

• Biomedical Science Letters
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• v.11 no.4
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• pp.447-453
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• 2005

A total of 108 strains of MRSA (Methicillin-resistant Staphylococcus aureus) clinical isolates was collected from $121^{st}$ general hospital (U.S. military hospital), Korean healthcare facility from January to March in 2005 and Wonju Christian hospital in 1999. Antimicrobial susceptibility test by Vitek System and MIC test using oxacillin and cephalothin stripes by E-test were executed. PCR based detection of mecA gene was performed on the all of MRSA clinical isolates, too. MRSA clinical isolates were characterized with antimicrobial resistance patterns, PCR based detection of mecA gene and validation of the multiplex PCR strategy of SCCmec among clinical isolates.

• Korean Journal of Head & Neck Oncology
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• v.30 no.2
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• pp.79-82
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• 2014

Salivary gland tumors take possession of almost 5% in head and neck malignancies. Among these, mucoepidermoid carcinoma(MEC) is most common malignany in major salivary glands(12-29%) and the parotid gland is most predilection site. Intra-oral MEC has a tendency to various locations, and the predilection sites are palate, cheek, mandible, lip and tongue in order of frequency. A few cases of MEC are occurred in with retromolar trigone, oropharynx, and ectopic salivary gland. Recently, we experienced a-65-year old woman with retromolar trigonal mass, and she was finally diagnosed as MEC. We report it with review of literature.

• International Journal of Contents
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• v.7 no.4
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• pp.103-107
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• 2011

The effective dose and the organ absorbed dose, which are given to a breast in the cases of using and not using the bismuth breast protection shield for the protection of a breast with the coronary artery CT angiography, have been measured and compared for the manual exposure control (MEC)and the automatic exposure control (AEC). In the cases of using and not using the bismuth breast protection shield, it has been found that the measured dose shows the reduction of about 23 to 26% for the MEC and about 22 to 25% for the AEC when the shield is used compared to the case of not using it. By comparing the shield and non-shield cases for the AEC and the MEC, it can be said that the value measured by carrying out the scanning process with the AEC mode has decreased by about 24 to 30% compared to the case of applying the MEC mode. Such a result shows that it is recommended to use the AEC mode for the reduction of the patient's exposure dose during the CT examination.

• Smart Media Journal
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• v.12 no.3
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• pp.68-76
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• 2023

Emerging mobile edge computing (MEC) can be used in battery-constrained Internet of things (IoT). The execution latency of IoT applications can be improved by offloading computation-intensive tasks to an MEC server. Recently, the popularity of unmanned aerial vehicles (UAVs) has increased rapidly, and UAV-based MEC systems are receiving considerable attention. In this paper, we propose a dynamic computation offloading paradigm for UAV-based MEC systems, in which a UAV flies over an urban environment and provides edge services to IoT devices on the ground. Since most IoT devices are energy-constrained, we formulate our problem as a Markov decision process considering the energy level of the battery of each IoT device. We also use model-free Q-learning for time-critical tasks to maximize the system utility. According to our performance study, the proposed scheme can achieve desirable convergence properties and make intelligent offloading decisions.