• Journal of Ship and Ocean Technology
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• 제11권4호
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• pp.55-71
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• 2007

The primary objective of this work is to develop methodologies for virtual model basin and to demonstrate the capabilities for generic multi-hull ship geometry. A computational fluid dynamics approach is used to simulate various model basin tests for steady resistance, maneuvering and seakeeping. For a catamaran hull configuration, the methodologies are used for solving these problems and the results are discussed. Computational results are compared with the results of a benchmarked potential flow theory method for calm water resistance.

• 한국군사과학기술학회지
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• 제20권2호
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• pp.197-205
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• 2017

During the sea trial, we discovered EMI between MOSCOS and ES DF antenna. CW emitted by MOSCOS raised the threshold level of ES DF antenna. As a result, direction finding rate of ES has been decreased. This is a study for the improvement of EMI between the antennas mounted on a surface ship. An analysis is accomplished for MOSCOS, ES DF antenna and Jamming transmitter. This paper presents the method how to solve EMI based on the measurements and calculations about the ES DF antenna receiving level, MOSCOS radiation pattern and Jamming transmitter thermal noise. The test was performed with optimal deployment of MOSCOS on a surface ship. After changing the position of MOSCOS, EMI has been decreased significantly.

• 시스템엔지니어링학술지
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• 제4권1호
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• pp.35-43
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• 2008

Generally, in analysis of reliability of Design and Development Phase, reliability of electrical components is analyzed based on standards such as MIL-HDBK-217F, Bellcore Issue 4,5,6 by analyzing stress of architectural side (Power, Voltage, Current and quality level of components) of weapon system and stress of operational side (operational environment, operational temperature, Operational Profile). But the reliability of mechanical components is analyzed based on the data book of failure history of mechanical parts called NPRD-95(Nonelectronic Parts Reliability Data-95) without any analysis of above stress. However, even if it's the same mechanical parts, it might have different failure rate(fatigue, wear, corrosion) during operation depending on how weary(stress : pressure, vibration, temperature during operation) the parts are. Therefore, analyzing reliability using just data book can cause big difference in reliability instead of analyzing based upon stressfulness that parts might have, operational concept, and other various factors. Thus, This paper will guide the way of predicting reliability by organizing ways of predicting reliability for system organization and adopt ing NSWC-98/LE1(Naval Surface Warfare Center-98/LE1) for mechanical components.

• 산업경영시스템학회지
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• 제44권1호
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• pp.9-16
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• 2021

Modern combat has been extended to the concept of real-time response to a variety of threats simultaneously occurring in vast areas. In order to quick command determination and accurate engagement in these threats, the combat system has emerged in frigate. Frigates conduct anti-surface, anti-submarine, and anti-aircraft as the core forces of the fleet. In this study, the combat effectiveness measures naval frigates using AHP (analytic hierarchy process) method. A hierarchical structure for measuring the combat effectiveness was developed, and weights of criteria were calculated by expert surveys and pair-wise comparisons. In addition, the combat effectiveness of frigates was synthesized and compared. The weights for each attribute were calculated, and the weights for the three main attributes were in the order of act (0.594), evaluate (0.277), and see (0.129). As a result of calculating the weight, anti surface warfare (0.203) was the highest. The combat effectiveness of FFG Batch-III, which has advanced hardware and software and improved combat system capabilities, see (1.73 to 2.56 times), evaluate (1.68 to 2.08 times), and act (1.31 to 3.80 times) better than the comparative frigate. In summarizing the combat effects of the frigate, FFG Batch-III was 1.41~2.95 times superior to the comparative frigate. In particular, a group of experts evaluated the act importantly, resulting in better combat effectiveness.

• Advances in Computational Design
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• 제1권3호
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• pp.235-251
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• 2016

We have performed a design optimization of a stiffened panel with curvilinear stiffeners using an artificial neural network (ANN) residual kriging based surrogate modeling approach. The ANN residual kriging based surrogate modeling involves two steps. In the first step, we approximate the objective function using ANN. In the next step we use kriging to model the residue. We optimize the panel in an iterative way. Each iteration involves two steps-shape optimization and size optimization. For both shape and size optimization, we use ANN residual kriging based surrogate model. At each optimization step, we do an initial sampling and fit an ANN residual kriging model for the objective function. Then we keep updating this surrogate model using an adaptive sampling algorithm until the minimum value of the objective function converges. The comparison of the design obtained using our optimization scheme with that obtained using a traditional genetic algorithm (GA) based optimization scheme shows satisfactory agreement. However, with this surrogate model based approach we reach optimum design with less computation effort as compared to the GA based approach which does not use any surrogate model.

• 대한기계학회논문집A
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• 제38권12호
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• pp.1379-1385
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• 2014

제품의 수명시험 전에 소요될 시험시간을 대강이라도 예측할 수 있으면 비용을 측정하여 시험을 어떻게 진행할지 판단하는데 도움이 된다. 기계부품의 경우의 신뢰도예측은 시스템의 고장 혹은 열화 메커니즘이 복잡하고 보편적인 데이터베이스가 존재하지 않기 때문에 수행이 난해하다. 본 연구는 유압 액추에이터와 공압 액추에이터에 각각 설치되는 탄성 U 형 씰을 대상으로 수명예측을 NSWC에서 제안하는 고장물리를 고려한 고장률 모형과 현장 데이터베이스를 활용하여 수행하였다. 그 결과들을 검증하기 위해 예측된 수명과 시험된 수명데이터들을 비교하였다. 본 연구는 고장률 모형에 포함된 각종 계수 값들을 결정하는 과정과 씰의 수명에 영향을 주는 인자들의 개별 민감도를 분석하고 그 미비점을 고찰하였다.

• 한국음향학회지
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• 제37권2호
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• pp.118-128
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• 2018

본 논문은 사이드 스캔 소나 영상을 컨볼루션 신경망으로 학습하여 수중물체를 탐색하는 방법을 다루었다. 사이드 스캔 소나 영상을 사람이 직접 분석하던 방법에서 컨볼루션 신경망 알고리즘이 보강되면 분석의 효율성을 높일 수 있다. 연구에 사용한 사이드 스캔 소나의 영상 데이터는 미 해군 수상전센터에서 공개한 자료이고 4종류의 합성수중물체로 구성되었다. 컨볼루션 신경망 알고리즘은 관심영역 기반으로 학습하는 Faster R-CNN(Region based Convolutional Neural Networks)을 기본으로 하며 신경망의 세부사항을 보유한 데이터에 적합하도록 구성하였다. 연구의 결과를 정밀도-재현율 곡선으로 비교하였고 소나 영상 데이터에 지정한 관심영역의 변경이 탐지성능에 미치는 영향을 검토함으로써 컨볼루션 신경망의 수중물체 탐지 적용성에 대해 살펴보았다.