• Journal of Ocean Engineering and Technology
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• v.36 no.5
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.8-14
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• 2020

The human impact of particulate matter are revealed and demand for improved forecast accuracy is increasing. Recently, efforts is made to improve the accuracy of PM10 predictions by using machine learning, but prediction performance is decreasing due to the particulate matter data with a large rate of low concentration occurrence. In this paper, separation prediction model by concentration is proposed to improve the accuracy of PM10 particulate matter forecast. The low and high concentration prediction model was designed using the weather and air pollution factors in Cheonan, and the performance comparison with the prediction models was performed. As a result of experiments with RMSE, MAPE, correlation coefficient, and AQI accuracy, it was confirmed that the predictive performance was improved, and that 20.62% of the AQI high-concentration prediction performance was improved.

• Asian-Australasian Journal of Animal Sciences
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• v.2 no.3
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• pp.407-408
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• 1989

• Computers and Concrete
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• v.6 no.3
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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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.

• Journal of Satellite, Information and Communications
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• v.6 no.1
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• pp.68-74
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• 2011

In this paper, we present the enhancement results such as availability and accuracy using the GPS L1 and Galileo E1 signal combination. To enhance the acquisition and tracking performance of signal processing in GNSS receiver. several tracking loops with integrator, discriminator, and loop filter module are applied. Also, this paper presents the performance comparison results between prototype receiver equipped with hardware board and software receiver. Also the tracking loop performance of real hardware receiver is verified by comparing with tracking accuracy, sensitivity occurred by the Spirent simulator. Especially, to process the Galileo E1 signal, it is used the a power early late type which is the typical type for DLL discriminator.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.6
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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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.8
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• pp.796-803
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• 2011

It is called self-alignment or initial alignment that INS(Inertial Navigation System) is aligned using the measurements from the inertial sensors as an accelerometer and a gyroscope and the inserted reference navigation information in the stop state. The main purpose of self-alignment is to obtain the initial attitude of INS. The accuracy of self-alignment is determined by the performance grade of the used inertial sensors, especially horizontal attitude accuracy by the horizontal accelerometer and vertical attitude accuracy by the E-axis gyroscope. Therefore the uncertain errors in the inertial sensors cause the performance of self-alignment to degrade. In this paper, we analyze theoretically and through a simulation how the errors of inertial sensors in the temperature stabilizing state, one of the uncertain errors, affect the accuracy of self-alignment.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.1
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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.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.177-183
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• 2024

The study investigates the impact of data quality on the performance of artificial intelligence (AI). To this end, the impact of labeling error levels on the performance of artificial intelligence was compared and analyzed through simulation, taking into account the similarity of data features and the imbalance of class composition. As a result, data with high similarity between characteristic variables were found to be more sensitive to labeling accuracy than data with low similarity between characteristic variables. It was observed that artificial intelligence accuracy tended to decrease rapidly as class imbalance increased. This will serve as the fundamental data for evaluating the quality criteria and conducting related research on artificial intelligence learning data.