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중국군의 전략지원부대가 한국안보에 미치는 영향과 시사점
이창형 ( Changhyung Lee ),박남태 ( Nam Tae Park ) 한국군사학회 2021 군사논단 Vol.105 No.-
This research aims at the analyses of Chinese Strategic Support Force(SSF) and its impacts and implications on Korean security. In 2015, Chinese military has established the Strategic Support Force. The backgrounds of establishing the force are to enhance of joint operation capability, facilitate the concept of informationized warfare, expand of geographical scope of Chinese military activities, implement of psychological warfare under informationized conditions. The SSF is composed of Space System and Network System Department, somewhat exclusive and independent each other, adding a few of general staff departments such as staff department and Political work department. The major mission of the SSF are strategic information support and information operation. The Space System Department controls and manages the Beidou(北斗) Satellite Position system, various military purpose satellites, and space related weapon systems. The Network System Department is responsible for cyber, electronic and psychological operations. Based on these analysis, the authors argue that it has significant influences on Korean military as well as the U.S. Forces in Korea, directly and indirectly. SSF is able to monitor activities of Korean military by using ISR equipped satellites. Regarding electronic warfare, the SSF's manned and unmanned electronic aircraft allow them to collect the electronic intelligence about the major military equipment not only of the Korean military but of the U.S. Forces in Korea. In this regard, it is necessary for the Korean military to develop a more concrete responsive plan against SSF's activities.
균형 훈련 플레이트 시스템을 이용한 생체역학적 특성 연구
전성철,임희철,이창형,김태호,정덕영,전경진,Jun, SungChul,Lim, HeeChul,Lee, ChangHyung,Kim, TaeHo,Jung, DukYoung,Chun, KeyoungJin 대한의용생체공학회 2015 의공학회지 Vol.36 No.5
The purpose of this study was to investigate the unstable plate system for the advanced balance ability. 7 male volunteers (age $33.7{\pm}1.2$ years, height $174.7{\pm}3.8cm$, weight $86.0{\pm}3.6kg$, BMI $28.2{\pm}2.0kg/m^2$) performed the partial squat motion on the shape of CAP type(${\cap}$) and BOWL type(${\cup}$) plate system. The range of motion (ROM) and muscle activation were acquired by the motion analysis system and the EMG system. Results of ROMs of the CAP type plate system were shown the widely range of the deviation in the ankle joint on the sagittal plane (sagittal plane - hip joint $10.7^{\circ}$ > $5.4^{\circ}$, knee joint $16.3^{\circ}$ > $6.4^{\circ}$, ankle joint $18.8^{\circ}$ > $6.3^{\circ}$ ; transverse plane - hip joint $3.5^{\circ}$ > $1.8^{\circ}$, knee joint $5.3^{\circ}$ > $3.4^{\circ}$, ankle joint $11.3^{\circ}$ > $5.3^{\circ}$ ; frontal plane - hip joint $0.9^{\circ}$ > $0.5^{\circ}$, knee joint $0.8^{\circ}$ > $0.6^{\circ}$, ankle joint $4.8^{\circ}$ > $3.7^{\circ}$). Muscle activation results of the CAP type plate system were indicated higher in major muscles for balance performance than the BOWL type plate system (vastus lateralis 0.90 > 0.62, peroneus longus 0.49 > 0.21, biceps femoris 0.38 > 0.14, gastrocnemius 0.11 > 0.05). These findings may indicate that the CAP type plate system would expect better effectiveness in perform the balance training. This paper is primary study for developing balance skills enhancement training device.
김진현(Jinhyun Kim),이창형(Changhyung Lee),심규석(Kyuseok Shim) 한국정보과학회 2014 정보과학회 컴퓨팅의 실제 논문지 Vol.20 No.3
하드웨어가 급속히 발전하고 SNS와 같이 사용자가 데이터를 생성하는 서비스가 늘어나며 다양한 분야에서 대규모의 시계열 데이터가 생성되고 있고 이들의 분석에 대한 요구가 커지고 있다. 본 논문에서는 다양한 어플리케이션에서 사용되는 시계열 데이터 예측을 위해 mRBF 함수를 사용하여 K - means 클러스터링 알고리즘을 변형한 시계열 데이터 클러스터링(clustering) 기술을 적용한 K -mRBF 모델을 제안한다. 실험에서는 실제 웹 서버 데이터센터에서 수집된 데이터와 합성 데이터를 이용하여 제안한 시계열 데이터 예측 방식의 정확성을 평가하고 기존의 최신 연구 기법에 비해 나은 성능을 보임을 확인한다. There is a wide range of applications such as social network services, sensor networks and data centers which generate time series data. Thus, analysis of such time series data has attracted a lot of attention in the recent years. In this paper, we propose a model called K-mRBF which utilizes a modified K-means clustering algorithm with the multivariate radial basis functions (mRBF) to predict future values based on previously observed values. We conduct extensive experiments using synthetic as well as real-life data sets to compare our K-mRBF model to the state-of-the-art model. Experimental results confirm the accuracy of our model compared to state-of-the-art models.