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        검색결과 4

        1.
        2015.05 KCI 등재 구독 인증기관 무료, 개인회원 유료
        The aim of this study is to find out the behavior and acoustic backscattering of the large jellyfish Nemopliema nomurai using hydroacoustics in situ. N. nomurai was distributed at depths ranging from 10~15 m during the day. Regarding the behavior of N. nomurai, there was no significant change in depth, and 3D tortuosity was not high. The vertical direction was ±10° from the horizontal, and moving speed was 0.9~1.5 m s–1.With regard to hydro–acoustical characteristics, the mean TS of N. nomurai ranged from –69.6~–56.0 dB at 38 kHz and –69.4~–54.5 dB at 120 kHz. TS variation (Max TS–Min TS) at 38 and 120 kHz was 0~10.2 dB and 0.2~16.0 dB, respectively. Mean TS and TS variation (Max TS–Min TS) of N. nomurai were higher at 120 kHz than at 38 kHz. The results showed that the use of hydroacoustics was effective in estimating the distribution depth, behavior, and acoustic characteristics of the target.
        4,000원
        2.
        2017.10 KCI 등재 서비스 종료(열람 제한)
        The tracking filter plays a key role in accurate estimation and prediction of maneuvering the vessel’s position and velocity. Different methods are used for tracking. However, the most commonly used method is the Kalman filter and its modifications. The α- β - γ filter is one of the special cases of the general solution provided by the Kalman filter. It is a third order filter that computes the smoothed estimates of position, velocity, and acceleration for the nth observation, and predicts the next position and velocity. Although found to track a maneuvering target with good accuracy than the constant velocity α - β filter, the α - β - γ filter does not perform impressively under high maneuvers, such as when the target is undergoing changing accelerations. This study aims to track a highly maneuvering target experiencing jerky motions due to changing accelerations. The α - β - γ filter is extended to include the fourth state that is, constant jerk to correct the sudden change of acceleration to improve the filter’s performance. Results obtained from simulations of the input model of the target dynamics under consideration indicate an improvement in performance of the jerky model, α - β - γ - η algorithm as compared to the constant acceleration model, α - β - γ in terms of error reduction and stability of the filter during target maneuver.
        3.
        2017.09 서비스 종료(열람 제한)
        As the Photovoltaic system market increases, various technologies are emerging to improve system operation efficiency. Such additional systems of the power generation system are generally referred to as ‘Balance of System’, for example a panel cooling, a panel cleaning and a panel angle adjusting apparatus. In this paper, we discuss an algorithm to calculate the target temperature of cooling in response to changes in the installation environment conditions of the power generation system so that the efficiency improvement rate target set by the user can be achieved with respect to the control method of the cooling water injection system among various panel cooling apparatuses. In order to calculate the target temperature of cooling, the output enhancement coefficient is calculated experimentally based on the temperature change according to the solar radiation condition of the PV panel, and the required reduction temperature of each irradiation condition is calculated considering the efficiency improvement rate. In addition, the efficiency improvement ratio is calculated considering the installation condition of the general power generation system without a separate control group. The thermal performance coefficient of the PV panel test body for calculating the expected temperature of the PV panel is calculated experimentally. The target temperature of cooling is calculated as the sum of the expected temperature of the PV panel and the required reduction temperature, and the injection system that tracks the target temperature by cooling water injection is constructed and compared with the power generation improvement rate and the user setting efficiency improvement rate.
        4.
        2015.08 KCI 등재 서비스 종료(열람 제한)
        ARPA(Automatic Radar Plotting Aid)는 자동레이더 플로팅 장치로써, 레이더 물표의 상대침로와 상대방위로 구성된 운동벡터에 본선의 침로와 방위로 구성되는 운동벡터를 가감 연산(벡터연산)하여, 물표의 진침로와 진방위 및 최근접점과 근접시간을 계산하는 장치를 말한다. 본 연구의 목적은 ARPA 레이더를 구현하기 위한 물표의 획득 및 추적 기술을 개발하는 것으로, 이에 관한 여러 선행 연구를 검토하 여 적용 가능한 알고리듬 및 기법을 조합하여 기초적인 ARPA 기능을 개발하였다. 주요 연구내용으로, 레이더 영상에서 물표를 획득하기 위 하여, 회색조 변환, 가운시안 평활 필터 적용, 이진화 및 라벨링(Labeling)과 같은 순차적 영상 처리 방법을 고안하였고, 이전 영상에서의 물표 가 다음 영상에서의 어느 물표인지를 결정하는데 근접이웃탐색알고리듬을 사용하였으며, 물표의 진침로와 진방위를 계산하는 거동해석에 칼 만필터를 사용하였다. 또한 이러한 기법을 전산 구현하여 실선실험을 수행하였고, 이를 통해 개발된 ARPA의 기능이 실용상 사용가능함을 검 증하였다.