• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.240-243
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• 2009

본 논문에서는 수중에서 이동하는 무인잠수정 및 수중이동체의 위치를 측정하는 방법 중의 하나인 동적 단기선 방식(SBL)에 의한 무인잠수정의 위치측정에 대한 방법을 하이드로폰과 DAQ(Data Aquisition) 시스템을 이용하여 수조에서 테스트를 수행하였고, 실 해역에서의 실험을 실시하였다. 실험을 위해서 4개의 센서가 수조의 벽면에 고정이 되어 있으며, 이동체와 고정된 4개의 센서가 신호를 송수신함으로써 상호간의 위치추적이 가능하게 하는 시뮬레이션을 실시하였으며, 제안하는 SBL시스템과 장기선 방식(Long baseline)을 비교하기위한 시뮬레이션을 실시하여 두 시스템을 비교하였다. 측정된 신호는 DAQ 시스템을 이용하여 데이터를 취득하였고, Labview 프로그램을 이용하여 실시간으로 무인잠수정의 위치를 추정하였다. 위치추정에 사용된 알고리즘은 삼각측량법에 의한 방법을 사용하였으며, X, Y방향에 대해서는 비교적 오차가 적은 추정 결과를 나타내었으나 Z방향에 대하여서는 큰 오차를 보여 데이터로 사용할 수 가 없었다. 이는 수중이동체의 수심측정 센서를 이용하여 보완할 수 있을 것으로 본다. 향후 연구로는 위치추정 알고리즘을 보완하여 실제 선박 선저부에 센서가 부착되었을 경우에 대한 적용연구를 진행할 예정이며, 위치추정 알고리즘을 발전시켜 3차원에서의 정확한 위치 추적을 가능하게 할 예정이다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.1
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• pp.27-32
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• 2016

In this paper, we proposed a soft decision-directed channel estimation based on MMSE estimation for MIMO-OFDM system. While the conventional method employs only pilot signals for channel estimation, the proposed algorithm performs channel estimation using pilot and reliable data signals. We also proposed selection criterion among reliable data signal for channel estimation. From numerical simulations, we show that the proposed channel estimator achieves 1 dB performance gain over conventional channel estimators.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.973-975
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• 2016

In this paper, we propose a selective extended Kalman filter based accurate pose estimation of the rigid body using a sensor fusion method. The pose of a rigid body can be estimated roughly by the Gauss-Newton method using the acceleration data and geomagnetic data, which can be refined with vision information and the gyro sensor information. However strong external interference noise makes the rough pose estimation difficult. In this paper, according to the measurement level of the external interference noise, the extended Kalman filter selectively uses mostly vision and gyro sensor information to increase the estimation credibility under strong interference noise environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8753-8759
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• 2015

Establishment of analysis procedures and validated performance measurements for generator output is required to maintain stable management of generator output in turbine power generation cycle. We developed turbine expansion model and measurement validation model for the performance calculation of generator using turbine output based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). We also developed verification model for uncertain measurement data related to the turbine and generator output. Although the model in previous researches was developed using artificial neural network and kernel regression, the verification model in this paper was based on algorithms through Support Vector Machine (SVM) model to overcome the problems of unmeasured data. The selection procedures of related variables and data window for verification learning was also developed. The model reveals suitability in the estimation procss as the learning error was in the range of about 1%. The learning model can provide validated estimations for corrective performance analysis of turbine cycle output using the predictions of measurement data loss.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.23-28
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• 2021

Data analysis research using deep learning has recently been studied in various field. In this paper, we conduct a GNSS (Global Navigation Satellite System)-based meteorological study applying deep learning by estimating the ZWD (Zenith tropospheric Wet Delay) through MLP (Multi-Layer Perceptron) and LSTM (Long Short-Term Memory) models. Deep learning models were trained with meteorological data and ZWD which is estimated using zenith tropospheric total delay and dry delay. We apply meteorological data not used for learning to the learned model to estimate ZWD with centimeter-level RMSE (Root Mean Square Error) in both models. It is necessary to analyze the GNSS data from coastal areas together and increase time resolution in order to estimate ZWD in various situations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2D
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• pp.319-327
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• 2009

In order to estimate accurate position by GPS observations, it is prerequisite to define both of the correct function model and the realistic stochastic model. In the case that un-modeled outliers exist in observations, estimates become biased, and their standard deviations are unable to be used as a measure which represents their accuracy. Hence, such outliers should be appropriately removed from the observations before estimating final solutions, so that the accuracy can be maximized with the improvement of the reliability. For this purpose, this research deals with quality control and quality measure computation algorithms for GPS stand-alone positioning. After theoretical studies, all the algorithms have been implemented and tested with real observations. Results of the tests indicate that the reliability of the estimated position is improved by increasing redundancy as well as using good satellite geometry and more realistic stochastic model. Moreover, the adaptation of the quality control procedure enable to improve positioning reliability and accuracy by appropriately excluding outlier in observations.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.1-10
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• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.

• Journal of KIISE:Databases
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• v.34 no.1
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• pp.84-97
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• 2007

Selectivity estimation techniques in query optimization have been used in commercial databases and histograms are popularly used for the selectivity estimation. Recently, the techniques for spatio-temporal databases have been restricted to existing temporal and spatial databases. In addition, the selectivity estimation techniques focused on time-series data such as moving objects. It is also impossible to estimate selectivity for range queries with a time interval. Therefore, we construct two histograms, CMH (current multidimensional histogram) and PMH (past multidimensional histogram), to estimate the selectivity of multidimensional sequence data in spatio-temporal databases and propose effective selectivity estimation methods using the histograms. Furthermore, we solve a problem about the range query using our proposed histograms. We evaluated the effectiveness of histograms for range queries with a time interval through various experimental results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.414-419
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• 2020

An artificial intelligence-based cable tension estimation model was developed to expand the utilization of data obtained from cable accelerometers of cable-stayed bridges. The model was based on an algorithm for selecting the natural frequency in the tension estimation process based on the vibration method and an applied artificial neural network (ANN). The training data of the ANN was composed after converting the cable acceleration data into the frequency, and machine learning was carried out using the characteristics with a pattern on the natural frequency. When developing the training data, the frequencies with various amplitudes can be used to represent the frequencies of multiple shapes to improve the selection performance for natural frequencies. The performance of the model was estimated by comparing it with the control criteria of the tension estimated by an expert. As a result of the verification using 139 frequencies obtained from the cable accelerometer as the input, the natural frequency was determined to be similar to the real criteria and the estimated tension of the cable by the natural frequency was 96.4% of the criteria.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.5
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• pp.599-606
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• 2022

Strain gauges and the bridge weigh-in-motion (BWIM) method are the representative field measurement methods used for fatigue evaluationsof a steel bridge-in-service. For a fatigue reliability evaluation to assess fatigue damage accumulation, the effective stress range and the number of stress cycles applied as the fatigue details can be estimated based on the AASHTO Manual for Bridge Evaluations with the field measurement data of the target bridge. However, the procedure for estimating the effective stress range and the stress cycles from field measurement data has not been explicitly presented. Furthermore, studies that quantitatively compare differences in fatigue evaluation results according to the field measurement data type or processing method used are still insufficient. Here, a fatigue reliability evaluation is conducted using strain and BWIM data that are measured simultaneously. A frame model and a shell-solid model were generated to examine the effect of the accuracy of the structural analysis model when using BWIM data. Also, two methods of handling BWIM data when estimating the effective stress range and average daily cycles are defined. As a result, differences in evaluation results according to the type of field measurement data used, the accuracy of the structural analysis model, and the data handling method could be quantitatively confirmed.