• Current Optics and Photonics
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• v.8 no.2
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• pp.183-191
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• 2024

High-resolution retinal imaging based on adaptive optics (AO) is important for early diagnosis related to retinal diseases. However, in practical applications, closed-loop AO correction takes a relatively long time, and traditional open-loop correction methods have low accuracy in correction, leading to unsatisfactory imaging results. In this paper, a SH-U-net-based open-loop AO wavefront correction method is presented for a retinal AO imaging system. The SH-U-net builds a mathematical model of the entire AO system through data training, and the Root mean square (RMS) of the distorted wavefront is 0.08λ after correction in the simulation. Furthermore, it has been validated in experiments. The method improves the accuracy of wavefront correction and shortens the correction time.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.5
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• pp.595-610
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• 2003

The statistical indexes such as RMSE (Root Mean Square Error), Mean Bias error, and IOA (Index of agreement) are used to evaluate 3 Dimensional wind and temperature fields predicted by operational meteorological model RAMS (Regional Atmospheric Meteorological System) implemented in CARIS (Chemical Accident Response Information System) for the dispersion forecast of hazardous chemicals in case of the chemical accidents in Korea. The operational atmospheric model, RAMS in CARIS are designed to use GDAPS, GTS, and AWS meteorological data obtained from KMA (Korean Meteorological Administration) for the generation of 3-dimensional initial meteorological fields. The predicted meteorological variables such as wind speed, wind direction, temperature, and precipitation amount, during 19 ∼ 23, August 2002, are extracted at the nearest grid point to the meteorological monitoring sites, and validated against the observations located over the Korean peninsula. The results show that Mean bias and Root Mean Square Error are 0.9 (m/s), 1.85 (m/s) for wind speed at 10 m above the ground, respectively, and 1.45 ($^{\circ}C$), 2.82 ($^{\circ}C$) for surface temperature. Of particular interest is the distribution of forecasting error predicted by RAMS with respect to the altitude; relatively smaller error is found in the near-surface atmosphere for wind and temperature fields, while it grows larger as the altitude increases. Overall, some of the overpredictions in comparisons with the observations are detected for wind and temperature fields, whereas relatively small errors are found in the near-surface atmosphere. This discrepancies are partly attributed to the oversimplified spacing of soil, soil contents and initial temperature fields, suggesting some improvement could probably be gained if the sub-grid scale nature of moisture and temperature fields was taken into account. However, IOA values for the wind field (0.62) as well as temperature field (0.78) is greater than the 'good' value criteria (> 0.5) implied by other studies. The good value of IOA along with relatively small wind field error in the near surface atmosphere implies that, on the basis of current meteorological data for initial fields, RAMS has good potentials to be used as a operational meteorological model in predicting the urban or local scale 3-dimensional wind fields for the dispersion forecast in association with hazardous chemical releases in Korea.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.3
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• pp.632-640
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• 2007

The low force estimation method of skeletal muscle was proposed by using ICA(independent component analysis) and neuro-transmission model. An EMG decomposition is the procedure by which the signal is classified into its constituent MUAP(motor unit action potential). The force index of electromyography was due to the generation of MUAP. To estimate low force, current analysis technique, such as RMS(root mean square) and MAV(mean absolute value), have not been shown to provide direct measures of the number and timing of motoneurons firing or their firing frequencies, but are used due to lack of other options. In this paper, the method based on ICA and chemical signal transmission mechanism from neuron to muscle was proposed. The force generation model consists of two linear, first-order low pass filters separated by a static non-linearity. The model takes a modulated IPI(inter pulse interval) as input and produces isometric force as output. Both the step and random train were applied to the neuro-transmission model. As a results, the ICA has shown remarkable enhancement by finding a hidden MAUP from the original superimposed EMG signal and estimating accurate IPI. And the proposed estimation technique shows good agreements with the low force measured comparing with RMS and MAV method to the input patterns.

• Microbiology and Biotechnology Letters
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• v.51 no.1
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• pp.69-82
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• 2023

Nitrilases are a hydrolase group of enzymes that catalyzes nitrile compounds and produce industrially important organic acids. The current objective is to optimize nitrilase production using statistical methods assisted with artificial intelligence (AI) tool from novel nitrile degrading isolate. A nitrile hydrolyzing bacteria Bacillus subtilis AGAB-2 (GenBank Ascension number- MW857547) was isolated from industrial effluent waste through an enrichment culture technique. The culture conditions were optimized by creating an orthogonal design with 7 variables to investigate the effect of the significant factors on nitrilase activity. On the basis of obtained data, an AI-driven support vector machine was used for the fitted regression, which yielded new sets of predicted responses with zero mean error and reduced root mean square error. The results of the above global optimization were regarded as the theoretical optimal function conditions. Nitrilase activity of 9832 ± 15.3 U/ml was obtained under optimized conditions, which is a 5.3-fold increase in compared to unoptimized (1822 ± 18.42 U/ml). The statistical optimization method involving Plackett Burman Design and Response surface methodology in combination with an AI tool created a better response prediction model with a significant improvement in enzyme production.

• Journal of Korea Water Resources Association
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• v.51 no.9
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• pp.747-759
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• 2018

Multivariate regional frequency analysis has advantages of regional and multivariate framework as adopting a large number of regional dataset and modeling phenomena that cannot be considered in the univariate frequency analysis. To the best of our knowledge, the multivariate regional frequency analysis has not been employed for hydrological variables in South Korea. Applicability of the multivariate regional frequency analysis should be investigated for the hydrological variable in South Korea in order to improve our capacity to model the hydrological variables. The current study focused on estimating parameters of regional copula and regional marginal models, selecting the most appropriate distribution models, and estimating regional multivariate growth curve in the multivariate regional frequency analysis. Annual maximum rainfall and duration data observed at 71 stations were used for the analysis. The results of the current study indicate that Frank and Gumbel copula models were selected as the most appropriate regional copula models for the employed regions. Several distributions, e.g. Gumbel and log-normal, were the representative regional marginal models. Based on relative root mean square error of the quantile growth curves, the multivariate regional frequency analysis provided more stable and accurate quantiles than the multivariate at-site frequency analysis, especially for long return periods. Application of regional frequency analysis in bivariate rainfall-duration analysis can provide more stable quantile estimation for hydraulic infrastructure design criteria and accurate modelling of rainfall-duration relationship.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1003-1021
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• 2015

MR dampers have been proposed for the control of cable vibration of cable-stayed bridge in recent years due to their high performance and low energy consumption. However, the highly nonlinear feature of MR dampers makes them difficult to be designed with efficient semi-active control algorithms. Simulation study has previously been carried out on the cable-MR damper system using a semi-active control algorithm derived based on the universal design curve of dampers and a bilinear mechanical model of the MR damper. This paper aims to verify the effectiveness of the MR damper for mitigating cable vibration through a full-scale experimental test, using the same semi-active control strategy as in the simulation study. A long stay cable fabricated for a real bridge was set-up with the MR damper installed. The cable was excited under both free and forced vibrations. Different test scenarios were considered where the MR damper was tuned as passive damper with minimum or maximum input current, or the input current of the damper was changed according to the proposed semi-active control algorithm. The effectiveness of the MR damper for controlling the cable vibration was assessed through computing the damping ratio of the cable for free vibration and the root mean square value of acceleration of the cable for forced vibration.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.108-113
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• 2005

In this study, rotary type diamond dressing system for ultra-precision centerless grinding for ferrule was developed at the first time and experiments were conducted with AE sensor and hall sensor system to verify the optimum dressing condition for ultra-precision centerless grinding for ferrule. The correlations with the condition of dressing are evaluated by AE signal analysis with root mean square (RMS) and frequency analysis. And current signals from hall sensor are also studied as a factor of dressing optimum condition selection. Dressing process was conducted to investigate the effects of depth of cut, rotating speed, and the number of overlap to select the optimum condition of rotary dressing system of ultra-precision centerless grinding machine for ferrule fabrication. In order to verify the optimum condition of dressing, AE and current signals were compared with the surface quality of dressing wheel and grinding wheel for ultra-precision ferrule grinding. All of these experiments were completed by Taguchi Methodology to reduce experimental time. Hence, the optimum condition of rotary dressing system for ultra-precision centerless grinding for ferrule fabrication can be selected following to the experiment result from signals of AE and hall sensor.

• Journal of Ocean Engineering and Technology
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• v.25 no.4
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• pp.59-65
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• 2011

In structural health monitoring (SHM) using electro-mechanical impedance signatures, it is a critical issue for extremely large structures to extract the best damage diagnosis results, while minimizing unknown environmental effects, including temperature, humidity, and acoustic vibration. If the impedance signatures fluctuate because of these factors, these fluctuations should be eliminated because they might hide the characteristics of the host structural damages. This paper presents a long-term SHM technique under an unknown noisy environment for tidal current power plant structures. The obtained impedance signatures contained significant variations during the measurements, especially in the audio frequency range. To eliminate these variations, a continuous principal component analysis was applied, and the results were compared with the conventional approach using the RMSD (Root Mean Square Deviation) and CC (Cross-correlation Coefficient) damage indices. Finally, it was found that this approach could be effectively used for long-term SHM in noisy environments.

• KIEE International Transactions on Power Engineering
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• v.3A no.4
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• pp.191-197
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• 2003

Accurate detection and classification of faults on transmission lines is vitally important. In this respect, many different types of faults occur, such as inter alia low impedance faults (LIF) and high impedance faults (HIF). The latter in particular pose difficulties for the commonly employed conventional overcurrent and distance relays, and if undetected, can cause damage to expensive equipment, threaten life and cause fire hazards. Although HIFs are far less common than LIFs, it is imperative that any protection device should be able to satisfactorily deal with both HIFs and LIFs. Because of the randomness and asymmetric characteristics of HIFs, their modeling is difficult and numerous papers relating to various HIF models have been published. In this paper, the model of HIFs in transmission lines is accomplished using the characteristics of a ZnO arrester, which is then implemented within the overall transmission system model based on the electromagnetic transients program (EMTP). This paper proposes an algorithm for fault detection and classification for both LIFs and HIFs using Adaptive Network-based Fuzzy Inference System (ANFIS). The inputs into ANFIS are current signals only based on Root-Mean-Square (RMS) values of 3-phase currents and zero sequence current. The performance of the proposed algorithm is tested on a typical 154 kV Korean transmission line system under various fault conditions. Test results demonstrate that the ANFIS can detect and classify faults including LIFs and HIFs accurately within half a cycle.

• Journal of Radiation Protection and Research
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• v.41 no.3
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• pp.191-195
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• 2016

Background: The current status of ACE format MCNP/MCNPX libraries by NDC of KAERI is presented with a short description of each library. Materials and Methods: Validation calculations with recent nuclear data evaluations ENDF/BV-II. 0, ENDF/B-VII.1, JEFF-3.2, and JENDL-4.0 have been carried out by the MCNP5 code for 119 criticality benchmark problems taken from the expanded criticality validation suite supplied by LANL. The overall performances of the ACE format KN-libraries have been analyzed in comparison with the results calculated with the ENDF/B-VII.0-based ENDF70 library of LANL. Results and Discussion: It was confirmed that the ENDF/B-VII.1-based KNE71 library showed better performances than the others by comparing the RMS errors and ${chi}^2$ values for five benchmark categories as well as whole benchmark problems. ENDF/B-VII.1 and JEFF-3.2 have a tendency to yield more reliable MCNP calculation results within certain confidence intervals regarding the total uncertainties for the $k_{eff}$ values. Conclusion: It is found that the adoption of the latest evaluated nuclear data might ensure better outcomes in various research and development areas.