• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.27-34
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• 2019

This paper presents a chamber pressure model development of K2 rifle by applying Gaussian mixture model. In order to materialize a real recoil force of a virtual reality shooting rifle in military combat training, the chamber pressure which is one of major components of the recoil force needs to be investigated and modeled. Over 200,000 data of the chamber pressure were collected by implementing live fire experiments with both K100 and M193 of 5.56 mm bullets. Gaussian mixture method was also applied to create a mathematical model that satisfies nonlinear, asymmetry, and deviations of the chamber pressure which is caused by irregular characteristics of propellant combustion. In addition, Polynomial and Fourier Regression were used for comparison of results, and the sum of squared errors, the coefficient of determination and root-mean-square errors were analyzed for performance measurement.

• Journal of the Korean Chemical Society
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• v.60 no.4
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• pp.225-234
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• 2016

The activity of 34 sulfonamide derivatives has been estimated by means of multiple linear regression (MLR), artificial neural network (ANN), simulated annealing (SA) and genetic algorithm (GA) techniques. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear -log (IC50) prediction. The results obtained using GA-ANN were compared with MLR-MLR, MLR-ANN, SA-ANN and GA-ANN approaches. A high predictive ability was observed for the MLR-MLR, MLR-ANN, SA-ANN and MLR-GA models, with root mean sum square errors (RMSE) of 0.3958, 0.1006, 0.0359, 0.0326 and 0.0282 in gas phase and 0.2871, 0.0475, 0.0268, 0.0376 and 0.0097 in solvent, respectively (N=34). The results obtained using the GA-ANN method indicated that the activity of derivatives of sulfonamides depends on different parameters including DP03, BID, AAC, RDF035v, JGI9, TIE, R7e+, BELM6 descriptors in gas phase and Mor 32u, ESpm03d, RDF070v, ATS8m, MATS2e and R4p, L1u and R3m in solvent. In conclusion, the comparison of the quality of the ANN with different MLR models showed that ANN has a better predictive ability.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.567-572
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• 2008

This paper's purpose is to improve determining of the critical response of curved bridge to multi-component seismic motion. There are several methods to combine responses by multi-component excitation response, 30%, 40% rules and square-root-of-sum (SRSS). These combination rules determine same value of critical response in straight bridges. However, each method has critical response value of different magnitude in curved bridges. Thus a study about critical response of curved bridges is required. This paper presents comparison critical responses value as each combination rule, 30%, 40% rules and SRSS on curved bridges with the different radiuses of curvature. This study was carried out by response spectrum analysis of OO IC steel box girder bridge using SAP2000. It is concluded as follows: 1) In curved bridges, 30% and 40% rules tend to underestimate the critical response relatively to SRSS. 2) When bridges have smaller radiuses than 100m, difference between SRSS and 30% or 40% rules let run errors up as radiuses of curvature decreased.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.9
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• pp.828-833
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• 2011

A reaction wheel assembly(RWA) is the largest disturbance source that can induce high frequency micro-vibration on an optical payload of satellites. To ensure a tight pointing-stability budget of satellites, the RWA disturbance effect on spacecraft should be accurately analyzed and evaluated for whole design phases. For this purpose, the micro-vibration disturbance of RWA should be precisely measured. In the present study, two measurement methods on RWA micro-vibration disturbances are compared and investigated. One is a free run-down speed test and the other is a constant speed test. The micro-vibration data measured by the two methods are analyzed in terms of spectrum characteristics, static and dynamic imbalance values, and root sum square(RSS) values. The analysis results show that both methods can measure very similar results in time and frequency domains and that the free run-down speed method is more adequate in respects to wheel friction modeling, noise rejection of imbalance and RSS peak evaluation.

• ETRI Journal
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• v.33 no.4
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• pp.487-496
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• 2011

Korea Multi-Purpose Satellite-5 (KOMPSAT-5) is the first satellite in Korea that provides 1 m resolution synthetic aperture radar (SAR) images. Precise orbit determination (POD) using a dual-frequency IGOR receiver data is performed to conduct high-resolution SAR images. We suggest orbit determination strategies based on a differential GPS technique. Double-differenced phase observations are sampled every 30 seconds. A dynamic model approach using an estimation of general empirical acceleration every 6 minutes through a batch least-squares estimator is applied. The orbit accuracy is validated using real data from GRACE and KOMPSAT-2 as well as simulated KOMPSAT-5 data. The POD results using GRACE satellite are adjusted through satellite laser ranging data and compared with publicly available reference orbit data. Operational orbit determination satisfies 5 m root sum square (RSS) in one sigma, and POD meets the orbit accuracy requirements of less than 20 cm and 0.003 cm/s RSS in position and velocity, respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.388-388
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• 2019

하도 홍수추적의 방법은 크게 수리학적 방법과 수문학적 방법으로 구분할 수 있다. 수리학적 홍수추적 방법은 정확하지만 대량의 자료가 필요하고 시간이 오래 걸린다. 이와 반대로 수문학적 홍수추적 방법은 정확성은 떨어지지만 소량의 자료만 있으면 되고 시간이 적게 걸린다. 여러 수문학적 홍수추적에 관한 연구들이 있으며 대표적으로 Muskingum 방법이 있다. Muskingum 방법 중 Linear Muskingum Model(LMM)은 방정식의 구조적 한계 때문에 정확한 홍수추적이 어려웠고, 이를 개선하기위하여 Nonlinear Muskingum Model(NLMM), Nonlinear Muskingum Model Incorporation Lateral Flow(NLMM-L) 및 Advanced Nonlinear Muskingum Model Incorporating Lateral Flow(ANLMM-L)이 제안되었다. 본 연구는 수문학적 홍수추적 중 Muskingum 방법의 결과 차이가 어떤 요인으로 인해 발생하는지 검토하였다. 최적화 알고리즘으로 화음탐색법(Harmony Search, HS)을 사용하였으며 LMM, NLMM, NLMM-L 및 ANLMM-L의 매개변수를 산정하였다. 각 방법에 적용 시 HS의 매개변수에 변화를 주어 민감도 분석을 실시하였으며, 분석을 위한 홍수자료는 The Willson Flood data (1947)를 선택하였다. 오차비교방법은 Sum of Squares(SSQ), Root Mean Square Errors(RMSE), Nash-Sutcliffe Efficiency(NSE)를 비교하였다. 비교 결과 알고리즘의 성능에 의한 차이보다 홍수추적 방법의 차이가 더 영향이 큰 것으로 나타났다.

• The Journal of the Convergence on Culture Technology
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• v.4 no.3
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• pp.299-303
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• 2018

Generally, when predicting the accuracy of the anti-air artillery system, the error is classified as fixed bias, variable bias, and random error. Then the standard deviation on the target is expressed as the square root of the squared sum of each error value which comes from the random error and variable bias and in the case of fixed bias, the mean value is shifted as the sum of errors from the fixed bias. At this time, the variables indicating the displacement of the direction of azimuth and elevation direction with regard to the change of the unit value of each error are weighted. These errors are then used to predict the system's delivery accuracy through a normally distributed integral. This paper presents a method of predicting system accuracy by considering the correlation of errors. This approach shows that it helps to predict the delivery accuracy of the system, precisely.

• Smart Media Journal
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• v.12 no.11
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• pp.134-144
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• 2023

Nowadays, Deep Learning (DL) technology is being used in several government departments. South Korea imports a lot of seafood. If the demand for fishery products is not accurately predicted, then there will be a shortage of fishery products and the price of the fishery product may rise sharply. So, South Korea's Ministry of Ocean and Fisheries is attempting to accurately predict seafood imports using deep learning. This paper introduces the solution for the fish import prediction in South Korea using the Long Short-Term Memory (LSTM) method. It was found that there was a huge gap between the sum of consumption and export against the sum of production especially in the case of two species that are Hairtail and Pollock. An import prediction is suggested in this research to fill the gap with some advanced Deep Learning methods. This research focuses on import prediction using Machine Learning (ML) and Deep Learning methods to predict the import amount more precisely. For the prediction, two Deep Learning methods were chosen which are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). Moreover, the Machine Learning method was also selected for the comparison between the DL and ML. Root Mean Square Error (RMSE) was selected for the error measurement which shows the difference between the predicted and actual values. The results obtained were compared with the average RMSE scores and in terms of percentage. It was found that the LSTM has the lowest RMSE score which showed the prediction with higher accuracy. Meanwhile, ML's RMSE score was higher which shows lower accuracy in prediction. Moreover, Google Trend Search data was used as a new feature to find its impact on prediction outcomes. It was found that it had a positive impact on results as the RMSE values were lowered, increasing the accuracy of the prediction.

• Journal of Korean Neurosurgical Society
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• v.47 no.5
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• pp.345-351
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• 2010

Objective : In this study, the Mindfulness Based Stress Reduction (MBSR) program was applied to patients presenting with depression and anxiety after surgery from spontaneous subarachnoid hemorrhage (SAH) and the effects were assessed. Methods : The subjects were patients admitted for cerebral aneurysm rupture and treated by means of surgery from March to December, 2007. More than 6 months had passed after surgery, without any special lesions showing up on computed tomography (CT), and the Glasgow outcome scale (GOS) was 5 points. Among patients with anxiety and depression symptoms, 11 patients completed the program. The MBSR program was conducted once a week, 2.5 hours each, for 8 weeks. The evaluation criteria were : 1) the Beck Depression Inventory (BDI): it measures the type and level of depression, 2) the State-Trait Anxiety Inventory : the anxiety state of normal adults without mental disorder, and 3) Heart Rate Variability (HRV) : the influence of the autonomous nervous system on the sinoarterial node varies continuously in response to the change of the internal/external environment. Results : The BDI value was decreased from 18.5 ${\pm}$ 10.9 to 9.5 ${\pm}$ 7.1 (p = 0.013) : it was statistically significant, and the depression level of patients was lowered. The state anxiety was decreased from 51.3 ${\pm}$ 13.9 to 42.3 ${\pm}$ 15.2; the trait anxiety was reduced from 50.9 ${\pm}$ 12.3 to 41.3 ${\pm}$ 12.8, and a borderline significant difference was shown (p = 0.091, p = 0.056). In other words, after the treatment, although it was not statistically significant, a decreased tendency in anxiety was shown. In the HRV measurement, standard deviation normal to normal (SDNN), square root of the square root of the mean sum of squared differences between adjacent normal to normal intervals (RMSSD), and total power (TP) showed significant increase, Physical Stress Index (PSI) showed a significant reduction, and thus an improvement in the homeostatic control mechanism of the autonomic nervous system was ween. Conclusion : The MBSR program was applied to the patients showing anxiety and depression reaction after SAH treatment, and a reduction in depression symptoms and physiological reactions were observed. The application of the MBSR program may be considered as a new tool in improving the quality of life for patients after surgery.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1041-1041
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• 2001

Removal of variability in spectra data before the application of chemometric modeling will generally result in simpler (and presumably more robust) models. Particularly for sparsely sampled data, such as typically encountered in diode array instruments, the use of Savitzky-Golay (S-G) derivatives offers an effective method to remove effects of shifting baselines and sloping or curving apparent baselines often observed with scattering samples. The application of these convolution functions is equivalent to fitting a selected polynomial to a number of points in the spectrum, usually 5 to 25 points. The value of the polynomial evaluated at its mid-point, or its derivative, is taken as the (smoothed) spectrum or its derivative at the mid-point of the wavelength window. The process is continued for successive windows along the spectrum. The original paper, published in 1964 [1] presented these convolution functions as integers to be used as multipliers for the spectral values at equal intervals in the window, with a normalization integer to divide the sum of the products, to determine the result for each point. Steinier et al. [2] published corrections to errors in the original presentation [1], and a vector formulation for obtaining the coefficients. The actual selection of the degree of polynomial and number of points in the window determines whether closely situated bands and shoulders are resolved in the derivatives. Furthermore, the actual noise reduction in the derivatives may be estimated from the square root of the sums of the coefficients, divided by the NORM value. A simple technique to evaluate the actual convolution factors employed in the calculation by the software will be presented. It has been found that some software packages do not properly account for the sampling interval of the spectral data (Equation Ⅶ in [1]). While this is not a problem in the construction and implementation of chemometric models, it may be noticed in comparing models at differing spectral resolutions. Also, the effects on parameters of PLS models of choosing various polynomials and numbers of points in the window will be presented.