Study on optimization of design parameters for offshore mooring system using sampling method

For the mooring system analysis, the safety of system should be firstly considered terms and conditions. By the reason of that, quantitative studies and evaluations about mooring system safety have been carried out. Some regulations and rules suggest a guides for mooring system design, for example, API(American Petroleum Institute) and DNV(Det Norske Veritas), of mooring system safety and performance limitation. There are a sort of parameters of mooring system, as widely known, those are weight and diameter of line components, length and stiffness of line. Use of those parameter value is up to the client or designer though more or less, generally the values of parameters are determined in accordance with the mooring system performance. And also the parameters have a different weight for mooring system, therefore the sensitivity study about design parameters is quite an important factor for mooring system design. In determining the parameters of mooring system, the probabilistic approach is one of the available solution. For the probabilistic approach, the probabilistic sampling method is derived and totally 50 scenarios are used for the sensitivity analysis. After the sensitivity study, optimization of mooring system design parameters and reliability analysis of mooring system is carried out.|계류시스템 설계 및 해석에 있어 시스템이 안정적으로 운영될 수 있도록 안전성이 가장 먼저 보장되어야 한다. API(American Petroleum Institute) 와 DNV(Det Norske Veritas)과 같은 관련 기관에서는 계류시스템의 설계에 관한 규정 및 설계 기준을 제시하고 있다. 계류라인을 구성하는 요소로는 계류선의 무게, 직경, 강성 등이 있으며 계류 시스템 상세 제원을 선택하는 것은 전적으로 디자이너의 판단에 근거하지만 계류 시스템 성능에 대한 해석 결과가 바탕이 되어야 한다. 계류시스템은 대상으로 하는 관련 기관에서 제시하는 설계 기준에 부합하는 결정론적 방법을 통한 해석이 보편적으로 수행되지만, 이는 계류시스템의 과도한 설계를 유발할 수 있다. 본 연구에서는 확률론적 해석 방법으로 발생할 수 있는 가능한 많은 경우의 수를 고려한 설계안 검토를 통한 설계 및 해석 절차를 사용하였다. 또한 계류시스템을 구성하는 설계 변수들은 대상으로 하는 목적 함수에 따라 차지하는 가중치는 상이하게 나타난다. 설계 기준이 되는 목적 함수에 대한 설계 변수의 민감도 측정 또한 계류 시스템 설계 시 고려되어야 할 항목이다. 따라서 최적의 계류 설계안 도출을 위해 계류 시스템에 대한 민감도 해석과 최적화 과정을 수행하였으며 신뢰성 기법을 통해 도출 결과의 적정성을 평가하였다.1. 서 론 1.1 연구배경 및 목적 ····················································································· 1 1.2 계류시스템 설계 개요 ··············································································· 1 1.3 연구내용 ······································································································· 2 2. 계류시스템 해석 2.1 대상 선형 및 계류시스템 해석 개요 ····················································· 4 2.2 대상선형 운동해석 ····················································································· 7 2.2.1 주파수 영역 해석 결과 ······································································ 9 2.3 계류시스템 초기 설계안 ········································································· 12 2.3.1 대상 해역 ···························································································· 12 2.3.2 환경 외력 ···························································································· 14 2.3.3 초기 설계안 ······················································································ 17 2.3.4 초기 설계안 해석 결과 – Intact condition ··································· 19 2.3.5 초기 설계안 해석 결과 – Damaged condition ···························· 24 3. 민감도 해석 및 최적화 설계 3.1 민감도 해석 및 최적화 설계 개요 ······················································· 30 3.1.1 대상 설계 변수 정의 ········································································ 30 3.1.2 표본 추출법 ························································································ 31 3.1.3 설계 변수의 확률분포 ······································································ 33 3.1.4 라틴 하이퍼큐브 샘플링 방법 ························································ 34 3.2 민감도 해석 ······························································································· 36 3.3 최적화 설계 ······························································································· 37 4. 결과 4.1 표본 해석 결과 ···················································································· 39 4.1.1 대상 표본 선정 ··········································································· 39 4.1.2 대상 표본 해석 ··········································································· 50 4.2 민감도 해석 결과 ················································································ 50 4.3 최적 설계안 도출 결과 ······································································ 51 4.4 최적 설계안 해석 결과 ······································································ 53 4.4.1 변동계수(Coefficient of Variation) 0.1 ··································· 53 4.4.2 변동계수(Coefficient of Variation) 0.2 ··································· 59 4.5 최적 설계안 해석결과 비교검토 ······················································ 64 5. 신뢰성 평가 5.1 신뢰성 평가 개요 ················································································ 65 5.2 계류 시스템 신뢰성의 기본 정의 ···················································· 66 5.3 계류선 장력, 파단강도 및 한계상태의 확률분포 ························· 66 5.4 신뢰성 평가 결과 ················································································ 69 6. 결론 ··············································································································· 70 References ········································································································ 73Maste

DSSS 시스템에서 위상 추정을 이용한 PN 신호의 빠른 포착 기법

Thesis (doctoral)--서울대학교 대학원 :전기.컴퓨터공학부,2001.Docto

Compensation of Magnetic Interference for Precise Directional Control of Drones

MasterMagnetometers are essential for directional control of drones. This paper proposes an eff icient algorithm for compensation of magnetic interference induced by the current owing in brushless DC (BLDC) motors of drones in order to achieve their precise directional control. To begin with, a state space model is constructed to involve a gyroscope as well as a magnetometer with its external interference. Based on the constructed state space model, the Extended Kalman Filter (EKF) is designed to estimate the direction of a drone by using measurements from two sensors, or the gyroscope and the magnetometer. By adjusting the measurement noise variance of the EKF according to thrust of BLDC motor, magnetic interference can be effectively rejected to increase the accuracy of direction estimation. In addition to direction estimation through a sensor fusion algorithm, online calibration is done periodically to update the elliptical locus of its measurements and hence reduce interference. The practical effectiveness of the compensation scheme for attenuating magnetic interference is illustrated through real experiments with a drone

low latency scheduling method on time division multiple access and a device for said method

본 발명에 따른, 버스를 서로 공유하는 노드(node)들의 버스 점유를 시분할 방식으로 중재하는 방법은, 버스를 공유하는 모든 노드들을 발견하는 1단계와, 상기 버스를 독점적으로 점유할 수 있는 데이터 채널이 상기 발견된 노드들의 각각에 대해 순차적으로 할당되고, 또한 상기 발견된 노드들이 자신에게 할당된 데이터 채널이 아님에도 버스를 점유할 수 있게 하는 가로채기(preemption) 구간이 적어도 하나 할당되는 데이터 사이클을 상기 버스에 반복적으로 형성하는 2단계를 포함하여 이루어진다. 여기서, 상기 발견된 노드들의 상기 가로채기 구간의 사용은, 상기 가로채기 구간에 대해서 상기 발견된 노드들의 적어도 일부에 대해 각각 정해지는 우선순위에 근거하여 결정되고, 상기 우선순위는 상기 가로채기 구간이 상기 데이터 채널들의 어디에 삽입 형성된 것인 지에 따라 상기 발견된 노드들의 적어도 일부에 변동되면서 정해진다

Low latency time-divisional data transmission method applicable to a transmission protocol complying with inter-frame gap and a device for said method

본 발명에 따른 기기는, 연속되는 데이터 프레임 간에는 최소한 유지되어야 하는 일정 간극을 준수하는 통신방식에 기반하여 데이터를 송수신하도록 의도된 통신모듈과 연결되어 그 통신모듈과 데이터를 송수신할 수 있도록 구성되고, 또한 버스에 형성되는 데이터 사이클의 일련의 시분할된 점유 구간들에서 자신에게 할당된 순서의 점유 구간을 사용하여 상기 통신모듈로부터 수신되는 데이터를 버스로 전송하고, 버스에서 검출하는 데이터를 상기 통신모듈로 전달할 수 있도록 구성된다. 그리고, 버스 상의 캐리어 존부를 알리기 위해 상기 통신모듈에 인가하고 있는 특정 신호에 대해서, 상기 일정 간극에 해당하는 시간과 상기 점유 구간에 해당하는 시간이 더해진 시구간 동안 캐리어가 없음을 알리는 상태, 예를 들어 LOW가 되게 하고, 상기 시구간의 이전과 이후로는 캐리어가 있음을 알리는 상태, 예를 들어 HIGH가 되게 하는 방식으로 상기 점유 구간에 동기하여 구동한다

Context Adaptive Personalized Psychological State Sampling Method and Apparatus for Wearable Devices

웨어러블 기기를 위한 상황 적응형 개인화 심리상태 표집 방법 및 장치가 제시된다. 본 발명에서 제안하는 웨어러블 기기를 위한 상황 적응형 개인화 심리상태 표집 장치는 모바일 및 웨어러블 기기를 활용하여 센서 데이터를 수집하는 센서 데이터 수집부, 수집된 센서 데이터를 활용하여 사용자의 신체활동을 추론하는 신체활동 분류부, 추론된 신체활동 별로 추출된 센서 데이터 특징 값을 활용하여 데이터를 군집화하는 센서 데이터 비지도학습부, 센서 데이터 비지도학습부를 통해 분류된 데이터에 기초하여 해당 센서 데이터로 현재의 심리상태를 추론하는 계층화 심리상태 분류부, 추론된 현재의 심리상태, 수집된 레이블 정보, 센서 데이터, 사용자 부담도 중 적어도 하나 이상에 기초하여 사용자에게 자가정보수집을 요청할지 여부를 결정하는 정보수집 요청 판단부 및 정보수집 요청 판단부에서 사용자에게 자가보고 정보를 수집하기로 결정할 경우 사용자로부터 자가보고 정보를 입력 받는 정보수집 인터페이스를 포함한다

Automotive SerDes Performance Evaluation under In-line Connector Channels

High-speed in-vehicle networks are required to process vast amounts of data from various sensors, such as cameras, light detection and ranging(LiDAR), and radars to develop autonomous driving technology. Automotive Serialized Deserializer (SerDes), such as flat panel display(FPD), gigabit multimedia serial link(GMSL), and Automotive SerDes Alliance (ASA), has been proposed in order to adopt high-speed SerDes technology to vehicles. In this paper, we investigate the optimal receiver environment parameters to provide the highest link performance for the Automotive SerDes channel under the in-line connectors mandatory for car assembly. In addition, we propose a channel estimation method by using auto-correlation in order to set the receiver analog front-end in an optimal way to achieve the highest signal-to-noise power ratio(SNPR) performance in various automotive link conditions. © 2020 Korean Society of Automotive Engineers. All rights reserved.1