• 한국해양공학회지
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• 제36권5호
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• pp.303-312
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• 2022

A submarine is optimized to operate below the water surface because it spends most of its time in a submerged condition. However, the performance in free surface conditions is also important because it is unavoidable for port departure and arrival. Generally, potential flow theory is used for seakeeping analysis of a surface ship and is known for excellent numerical accuracy. In the case of a submarine, the accuracy of potential theory is high underwater but is low in free surface conditions because of the nonlinearity near the free surface area. In this study, the seakeeping performance of a Canadian Victoria Class submarine in regular waves was investigated to improve the numerical accuracy in free surface conditions by using computational fluid dynamics (CFD). The results were compared to those of model tests. In addition, the potential theory software Hydrostar developed by Bureau Veritas was also used for seakeeping performance to compare with CFD results. From the calculation results, it was found that the seakeeping analysis by using CFD gives good results compared with those of potential theory. In conclusion, seakeeping analysis based on CFD can be a good solution for estimating the seakeeping performance of submarines in free surface conditions.

• 제어로봇시스템학회논문지
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• 제7권6호
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• pp.518-522
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• 2001

The main problem of the calibration of robots is to measure the position and orientation of a robot end effector. The calibration methods can be used as tool to improve the accuracy of robots without change of the arm or control architecture or robots. But such calibration methods require accurate measurements. Dynamic measurement of position and orientation provides a solution for this problem and improves dynamic accuracy by dynamic calibration of robots. This paper describes the development of the laser tracking system capable of determining the static and dynamic performance of industrial robots. The structure and systems components are presented and basic experimental results are included to demonstrated the instrument performance. The system can be applied to the remote controlled mobile robots as well s the calibration of robots.

• 한국정보통신학회논문지
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• 제24권1호
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• pp.8-14
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• 2020

미세먼지의 인체 영향이 밝혀지며 예보정확도 개선에 대한 요구가 증가하고 있다. 이에 기계 학습 기법을 도입하여 예측 정확성을 높이려는 노력이 수행되고 있으나, 저농도 발생 비율이 매우 큰 미세먼지 데이터로 인해 전체 예측 성능이 떨어지는 문제가 있다. 본 논문에서는 PM10 미세먼지 예보 정확도 향상을 위해 농도별 분리 예측 모델을 제안한다. 이를 위해 천안 지역의 기상 및 대기오염 인자를 활용하여 저, 고농도별 예측 모델을 설계하고 전 영역 예측 모델과의 성능 비교를 수행하였다. RMSE, MAPE, 상관계수 및 AQI 정확도를 통한 성능 비교 결과, 전체 기준에서 예측 성능이 향상됨을 확인하였으며, AQI 고농도 예측 성능의 경우 20.62%의 성능 향상이 나타났음을 확인하였다.

• Asian-Australasian Journal of Animal Sciences
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• 제2권3호
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• pp.407-408
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• 1989

• Computers and Concrete
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• 제6권3호
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• pp.203-223
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• 2009

Regression analysis (RA) can establish an explicit formula to predict the strength of High-Performance Concrete (HPC); however, the accuracy of the formula is poor. Back-Propagation Networks (BPNs) can establish a highly accurate model to predict the strength of HPC, but cannot generate an explicit formula. Genetic Operation Trees (GOTs) can establish an explicit formula to predict the strength of HPC that achieves a level of accuracy in between the two aforementioned approaches. Although GOT can produce an explicit formula but the formula is often too complicated so that unable to explain the substantial meaning of the formula. This study developed a Backward Pruning Technique (BPT) to simplify the complexity of GOT formula by replacing each variable of the tip node of operation tree with the median of the variable in the training dataset belonging to the node, and then pruning the node with the most accurate test dataset. Such pruning reduces formula complexity while maintaining the accuracy. 404 experimental datasets were used to compare accuracy and complexity of three model building techniques, RA, BPN and GOT. Results show that the pruned GOT can generate simple and accurate formula for predicting the strength of HPC.

• 산업융합연구
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• 제22권1호
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• pp.177-183
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• 2024

본 연구는 데이터의 품질이 인공지능(AI) 성능에 미치는 영향을 검토한다. 이를 위해, 데이터 특성변수(Feature)의 유사도와 클래스(Class) 구성의 불균형을 고려한 모의실험(Simulation)을 통해 라벨링 오류 수준이 인공지능의 성능에 미치는 영향을 비교 분석하였다. 그 결과, 특성변수 간 유사성이 높은 데이터에서는 특성 변수 간 유사성이 낮은 데이터에 비해 라벨링 정확도에 더 민감하게 반응하였으며, 클래스 불균형이 증가함에 따라 인공지능 정확도가 급격히 감소되는 경향을 관찰하였다. 이는 인공지능 학습데이터의 품질평가 기준 및 관련 연구를 위한 기초자료가 될 것이다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.10-14
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• 2005

This report deals with a feeding system of the Coaxal grinding machine, processing optical ferrule. This report also examines the applicability of using the feeding system for the Coaxial grinding machine, by mean of conducting performance estimation. The results are as follow; Repeatability of regulating wheel is $17{\mu}m$, R/W rotation accuracy is between $30\;\~\;40{\mu}m$. This means 'Rotation accuracy' is lower than the concentricity level. Backlash generation level at the feeding system of the grinding wheel is under $1{\mu}m$, thereby positioning accuracy is controlled within $2{\mu}m$ In terms of repeatability, you can find occasional error at the returning process from the starting point. This error is resulted from the measurement tolerance of the starting point sensor. We will get the repeatability level under control by $1{\mu}m$, through improving the soft-ware used and up-grading the sensor at the starting point.

• 한국항공우주학회지
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• 제39권8호
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• pp.796-803
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• 2011

정지 상태에서 관성항법장치를 구성하는 가속도계 및 자이로 측정치를 이용하여 초기 자세를 구하는 것을 초기정렬 혹은 자가정렬이라 한다. 초기정렬의 정밀도는 관성항법장치에 탑재되는 관성센서의 성능에 의하여 결정되며 수평축 자세는 수평축 가속도계, 수직축 자세는 E축 자이로 성능에 의해 결정된다. 그러므로 관성센서에서 발생된 불확실한 오차는 초기정렬의 정밀도를 저하시키는 주요원인이 된다. 논 논문에서는 관성센서의 불확실한 오차 중에서 관성항법장치에 전원이 인가되어 온도가 안정화 되는 상태에서의 관성센서 오차가 초기정렬 성능에 어떠한 영향을 미치는 가를 이론적으로 분석하고 시뮬레이션을 통하여 검증한 결과를 제시한다.

• Journal of Positioning, Navigation, and Timing
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• 제11권1호
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• pp.23-28
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• 2022

The eLoran system is a high-power terrestrial navigation system that is recognized as the most appropriate alternative to complement the GNSS's vulnerability to radio frequency interference. Accordingly, Korea has conducted eLoran technology development projects since 2016. The eLoran system developed in Korea provides 20 m positioning accuracy to maritime user in Incheon and Pyeongtaek harbor. To accurately calculate the position with the eLoran signal, it is necessary to apply a compensation method that mitigates the propagation delay. In this paper, we develop the compensation method to mitigate the eLoran signal propagation delay and evaluate the positioning performance in Incheon harbor. The propagation delay due to the terrain characteristics is pre-surveyed and stored in the user receiver. Real-time fluctuations in propagation delay compared to the pre-stored data are mitigated by the temporal correction generated at a nearby differential Loran station. Finally, two performance evaluation tests were performed to verify the positioning accuracy of the Korean eLoran system. The first test took place in December 2020 and the second in April 2021. As a result, the Korean eLoran service has been confirmed to provide 20 m location accuracy without GPS.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.408-408
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• 2000

A parabolic dish concentrator used in a high temperature application of solar energy tracks the sun's movement by two axis sun tracking system. In such a system, sun tracking performance affects the system efficiency directly. Generally the higher the tracking accuracy is, the better the system performance is. A large number of parabolic dish type concentrators has been developed and implemented in the world. However none of them clearly provided a qualitative method of how the accuracy of the sun tracking system can be evaluated. The work presented here is the evaluation of sun tracking performance of parabolic dish concentrator, which follows the sun's movement by the sensor, using computer vision system. We install a camera on the parabolic dish concentrator. While the concentrator follows the sun, sun's images are captured continuously. Then the performance of sun tracking system was evaluated by analyzing the variation of the position of the sun in the images.