• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2612-2616
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• 2003

Method for performance monitoring and diagnosis of a MIMO control system has been studied aiming at application to model predictive control (MPC) for industrial processes. The performance monitoring part is designed on the basis of the traditional SPC/SQC method. To meet the underlying premise of Schwart chart observation that the observed variable should be univariate and independent, the process variables are decorrelated temporally as well as spatially before monitoring. The diagnosis part was designed to identify the root of performance degradation among the controller, process, and disturbance. For this, a method to estimate the model-error and disturbance signal has been devised. The proposed methods were evaluated through numerical examples.

• Bulletin of the Korean Mathematical Society
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• v.60 no.1
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• pp.93-111
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• 2023

In this paper we study a nudging continuous data assimilation algorithm for the three-dimensional Leray-α model, where measurement errors are represented by stochastic noise. First, we show that the stochastic data assimilation equations are well-posed. Then we provide explicit conditions on the observation density (resolution) and the relaxation (nudging) parameter which guarantee explicit asymptotic bounds, as the time tends to infinity, on the error between the approximate solution and the actual solution which is corresponding to these measurements, in terms of the variance of the noise in the measurements.

• Journal of The Korean Society of Integrative Medicine
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• v.7 no.4
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• pp.119-130
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• 2019

Purpose : The purpose of this study was to investigate the meta-analysis on the effects of action observation training on stroke patients' walking. Methods : Domestic databases (DBpia, KISS, NDSL, and RISS) were searched for studies that conducted randomized controlled trials (RCTs) associated with action observation training in adults after stroke. The search outcomes were items associated with the walking function. The 18 studies that were included in the study were analyzed using R meta-analysis. A random-effect model was used for the analysis of the effect size because of the significant heterogeneity among the studies. Sub-group and meta-regression analysis were also used. Egger's regression test was conducted to analyze the publishing bias. Cumulative meta-analysis and sensitivity analysis were also done to analyze a data error. Results : The mean effect size was 2.77. The sub-group analysis showed a statistical difference in the number of training sessions per week. No statistically significant difference was found in the meta-regression analysis. Publishing bias was found in the data, but the results of the trim-and-fill method showed that such bias did not affect the obtained data. Also, the cumulative meta-analysis and sensitivity analysis showed no data errors. Conclusion : The meta-analysis of the studies that conducted randomized clinical trials revealed that action observation training effectively improved walking of the chronic stroke patients.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.1
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• pp.11-18
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• 2022

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

• Journal of the Korea Organic Resources Recycling Association
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• v.32 no.3
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• pp.19-29
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• 2024

Total organic carbon (TOC) represents the total amount of organic carbon contained in water and is a key water quality parameter used, along with biochemical oxygen demand (BOD) and chemical oxygen demand (COD), to quantify the amount of organic matter in water. In this study, a model to predict TOC was developed using XGBoost (XGB), a representative ensemble machine learning algorithm. Independent variables for model construction included water temperature, pH, electrical conductivity, dissolved oxygen concentration, BOD, COD, suspended solids, total nitrogen, total phosphorus, and discharge. To quantitatively analyze the impact of various water quality parameters used in model construction, the feature importance of input variables was calculated. Based on the results of feature importance analysis, items with low importance were sequentially excluded to observe changes in model performance. When built by sequentially excluding items with low importance, the performance of the model showed a root mean squared error-observation standard deviation ratio (RSR) range of 0.53 to 0.55. The model that applied all input variables showed the best performance with an RSR value of 0.53. To enhance the model's field applicability, models using relatively easily measurable parameters were also built, and the performance changes were analyzed. The results showed that a model constructed using only the relatively easily measurable parameters of water temperature, electrical conductivity, pH, dissolved oxygen concentration, and suspended solids had an RSR of 0.72. This indicates that stable performance can be achieved using relatively easily measurable field water quality parameters.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.46.3-47
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• 2021

Recently a multilayer spectral inversion (MLSI) model has been proposed to infer the physical parameters of plasmas in the solar chromosphere. The inversion solves a three-layer radiative transfer model using the strong absorption line profiles, H alpha and Ca II 8542 Å, taken by the Fast Imaging Solar Spectrograph (FISS). The model successfully provides the physical plasma parameters, such as source functions, Doppler velocities, and Doppler widths in the layers of the photosphere to the chromosphere. However, it is quite expensive to apply the MLSI to a huge number of line profiles. For example, the calculating time is an hour to several hours depending on the size of the scan raster. We apply deep neural network (DNN) to the inversion code to reduce the cost of calculating the physical parameters. We train the models using pairs of absorption line profiles from FISS and their 13 physical parameters (source functions, Doppler velocities, Doppler widths in the chromosphere, and the pre-determined parameters for the photosphere) calculated from the spectral inversion code for 49 scan rasters (~2,000,000 dataset) including quiet and active regions. We use fully connected dense layers for training the model. In addition, we utilize a skip connection to avoid a problem of vanishing gradients. We evaluate the model by comparing the pairs of absorption line profiles and their inverted physical parameters from other quiet and active regions. Our result shows that the deep learning model successfully reproduces physical parameter maps of a scan raster observation per second within 15% of mean absolute percentage error and the mean squared error of 0.3 to 0.003 depending on the parameters. Taking this advantage of high performance of the deep learning model, we plan to provide the physical parameter maps from the FISS observations to understand the chromospheric plasma conditions in various solar features.

• Atmosphere
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• v.23 no.2
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• pp.171-186
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• 2013

Recently, the number of observations used in a data assimilation system is increasing due to the enormous amount of observations, including satellite data. However, it is not clear that all of these observations are always beneficial to the performance of the numerical weather prediction (NWP). Therefore, it is important to evaluate the effect of observations on these forecasts so that the observations can be used more usefully in NWP process. In this study, the adjoint-based Forecast Sensitivity to Observation (FSO) method with the KMA Unified Model (UM) is applied to two high-impact weather events which occurred in summer and winter in Korea in an effort to investigate the effects of observations on the forecasts of these events. The total dry energy norm is used as a response function to calculate the adjoint sensitivity. For the summer case, TEMP observations have the greatest total impact while BOGUS shows the greatest impact per observation for all of the 24-, 36-, and 48-hour forecasts. For the winter case, aircraft, ATOVS, and ESA have the greatest total impact for the 24-, 36-, and 48-hour forecasts respectively, while ESA has the greatest impact per observation. Most of the observation effects are horizontally located upwind or in the vicinity of the Korean peninsula. The fraction of beneficial observations is less than 50%, which is less than the results in previous studies. As an additional experiment, the total moist energy norm is used as a response function to measure the sensitivity of 24-hour forecast error to observations. The characteristics of the observation impact with the moist energy response function are generally similar to those with the dry energy response function. However, the ATOVS observations were found to be sensitive to the response function, showing a positive (a negative) effect on the forecast when using the dry (moist) norm for the summer case. For the winter case, the dry and moist energy norm experiments show very similar results because the adjoint of KMA UM does not calculate the specific humidity of ice properly such that the dry and moist energy norms are very similar except for the humidity in air that is very low in winter.

• The Journal of the Acoustical Society of Korea
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• v.27 no.8
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• pp.443-449
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• 2008

One of the key issues in practical speech processing is to achieve robust Voice Activity Deteciton (VAD) against the background noise. Most of the statistical model-based approaches have tried to employ equally weighted likelihood ratios (LRs), which, however, deviates from the real observation. Furthermore voice activities in the adjacent frames have strong correlation. In other words, the current frame is highly correlated with previous frame. In this paper, we propose the effective VAD approach based on a minimum classification error (MCE) method which is different from the previous works in that different weights are assigned to both the likelihood ratio on the current frame and the decision statistics of the previous frame.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.53-76
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• 2020

The synchronous phasor measurement algorithm is the core content of the phasor measurement unit. This manuscript proposes a dynamic synchronous phasor measurement algorithm based on compressed sensing theory. First, a dynamic signal model based on the Taylor series was established. The dynamic power signal was preprocessed using a least mean square error adaptive filter to eliminate interference from noise and harmonic components. A Chirplet overcomplete dictionary was then designed to realize a sparse representation. A reduction of the signal dimension was next achieved using a Gaussian observation matrix. Finally, the improved orthogonal matching pursuit algorithm was used to realize the sparse decomposition of the signal to be detected, the amplitude and phase of the original power signal were estimated according to the best matching atomic parameters, and the total vector error index was used for an error evaluation. Chroma 61511 was used for the output of various signals, the simulation results of which show that the proposed algorithm cannot only effectively filter out interference signals, it also achieves a better dynamic response performance and stability compared with a traditional DFT algorithm and the improved DFT synchronous phasor measurement algorithm, and the phasor measurement accuracy of the signal is greatly improved. In practical applications, the hardware costs of the system can be further reduced.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.2
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• pp.98-109
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• 2010

This paper deals with a new method of unmanned aerial vehicle (UAV) recovery when a UAV fails to get a global positioning system (GPS) signal at an unprepared site. The proposed method is based on the simultaneous localization and mapping (SLAM) algorithm. It is a process by which a vehicle can build a map of an unknown environment and simultaneously use this map to determine its position. Extensive research on SLAM algorithms proves that the error in the map reaches a lower limit, which is a function of the error that existed when the first observation was made. For this reason, the proposed method can help an inertial navigation system to prevent its error of divergence with regard to the vehicle position. In other words, it is possible that a UAV can navigate with reasonable positional accuracy in an unknown environment without the aid of GPS. This is the main idea of the present paper. Especially, this paper focuses on path planning that maximizes the discussed ability of a SLAM algorithm. In this work, a SLAM algorithm based on extended Kalman filter is used. For simplicity's sake, a blimp-type of UAV model is discussed and three-dimensional pointed-shape landmarks are considered. Finally, the proposed method is evaluated by a number of simulations.