• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2002.11a
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• pp.97-117
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• 2002

A chemoinfometrical method for quantitative determination of crystal content of indomethacin (IMC) polymorphs based on fourie-transformed near-infrared (FT-NIR) spectroscopy was established. A direct comparison of the data with the ones collected from using the conventional powder X-ray diffraction method was performed. Pure $\alpha$ and ${\gamma}$ forms of IMC were prepared using published methods. Powder X-ray diffraction profiles and NIR spectra were recorded for six kinds of standard materials with various content of ${\gamma}$ form IMC. The principal component regression (PCR) analyses were performed based on normalized NIR spectra sets of standard samples of known content of IMC ${\gamma}$ form. A calibration equation was determined to minimize the root mean square error of the prediction. The predicted ${\gamma}$ form content values were reproducible and had a relatively small standard deviation. The values of ${\gamma}$ form content predicted by two methods were in close agreement. The results were indicated that NIR spectroscopy provides for an accurate quantitative analysis of crystallinity in polymorphs compared with the results obtained by conventional powder X-ray diffractometry.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.383-384
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• 2016

The Special Needs Education Assessment Tool (SNEAT) were verified of reliability and validity. However, the reliability and validity has been verified is only Okinawa Prefecture, the national data has not been analyzed. Therefore, this study aimed to verify the reliability and construct validity of SNEAT in Miyagi Prefecture as part of the national survey. SNEAT using 55 children collected from the classes on independent activities of daily living for children with disabilities in Miyagi Prefecture between November and December 2015. Survey data were collected in a longitudinal prospective cohort study. The reliability of SNEAT was verified via the internal consistency method; the coefficient of Cronbach's ${\alpha}$ were over 0.7. The validity of SNEAT was also verified via the latent growth curve model. SNEAT is valid based on its goodness-of-fit values obtained using the latent growth curve model, where the values of comparative fit index (0.997), tucker-lewis index (0.996) and root mean square error of approximation (0.025) were within the goodness-of-fit range. These results indicate that SNEAT has high reliability and construct validity.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.387-388
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• 2016

Kwon (2015) was developed Employment Promotion Tool for Persons with Disabilities in the Aspect of the Quality of Life(QOL-EPAT). But its reliability and validity have not been verified yet. Therefore, this study aimed to verify the reliability, content validity and construct validity of QOL-EPAT. This study was conducted with a disability employment specialists. Period May to October 2015, six months, was distributed to collect the questionnaire. Reliability of QOL-EPAT was estimated using the internal consistency method; both the coefficient of Cronbach's ${\alpha}$ were over 0.7. Construct Validity; Construct validity was verified using structural equation modeling (SEM). Goodness of fit index (GFI), Adjusted goodness of fit index (AGFI), comparative fit index (CFI), tucker-Lewis index (TLI) and root mean square error of approximation (RMSEA) are the suitability indices of SEM. As the result, GFI=0.898; AGFI=0.844; CFI=0.961; TLI=0.949 and RMSEA=0.069. The validity was verified because the values of GFI, AGFI, CFI, TLI and RMSEA were within the goodness-of-fit range. Thus, impaired employs promoters of Japan also provided which allows for analysis of the policy by using a validated scale.

• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.53-54
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• 2015

The maritime industry is expanding at an alarming rate and as such there is a perpetual need to improve situation awareness in the maritime environment using new and emerging technology. Tracking is one of the numerous ways of enhancing situation awareness by providing information that may be useful to the operator. The tracking system described herein comprises determining existing states of own ship, state prediction and state compensation caused by random noise. The purpose of this paper is to analyze the process of tracking and develop a tracking algorithm by using ${\alpha}-{\beta}-{\gamma}$ tracking filter under a random noise or irregular motion for use in a warship. The algorithm involves initializing the input parameters of position, velocity and course. The actual positions are then computed for each time interval. In addition, a weighted difference of the observed and predicted position at the nth observation is added to the predicted position to obtain the smoothed position. This estimation is subsequently employed to determine the predicted position at (n+1). The smoothed values, predicted values and the observed values are used to compute the twice distance root mean square (2drms) error as a measure of accuracy of the tracking module.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.55-57
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• 2015

The tracking system plays a key role in accurate estimation and prediction of maneuvering vessel's position and velocity in a bid to enhance safety by taking avoiding action against collision. Therefore, in order to achieve this, many ocean- going vessels are equipped with radar and the ARPA system. However, the accuracy of prediction highly depends on the choice of the gain parameters, ${\alpha}$, ${\beta}$ and ${\gamma}$ employed in the tracking filter. P revious research of this paper was based on theoretically developing an algorithm for a tracking module. This research paper is hence a continuation by the authors to determine the optimal values of the gain parameters used in the tracking module. A tracking algorithm is developed using the ${\alpha}-{\beta}-{\gamma}$ filter to carry out prediction and smoothing of the positions and velocities. Numerical simulations are then performed to evaluate the optimal values of the smoothing parameters that will improve the performance of the tracking module and reduce measurement noise. The twice distance root mean square (2drms) is then calculated to determine error variation.

• ETRI Journal
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• v.40 no.1
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• pp.122-132
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• 2018

This paper proposes a hierarchical dual filtering (HDF) algorithm to estimate the spatial region between a Cloud of Things (CoT) gateway and an Internet of Things (IoT) device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM) with a raw Bluetooth received signal strength indicator (RSSI). However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high-frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE) of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.

• Geomechanics and Engineering
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• v.9 no.1
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• pp.115-124
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• 2015

Determination of mobilized shear strength parameters (that identify stresses on the failure plane) is required for analyzing the stability by limit equilibrium method. Generalized Hoek-Brown (GHB) and Mohr-Coulomb (MC) failure criteria are usually used for obtaining stresses on the plane of failure. In the present paper, the applicability of these criteria for determining the stresses on failure plane is investigated. The comparison is based on stresses on the real failure plane which are obtained from the Mohr stress circle. To do so, 18 sets of data (consist of principal stresses and angle of failure plane) presented in the literature are used. In addition, the values account for (VAF) and the root mean square error (RMSE) indices were calculated to check the determination performance of the obtained results. Values of VAF and RMSE for the normal stresses on the failure plane evaluated from MC are 49% and 31.5 where for GHB are 55% and 30.5, respectively. Also, for the shear stresses on failure plane, they are 74% and 36 for MC, 76% and 34.5 for GHB. Results show that the obtained stresses and angles of failure plane for each criterion differ from real ones, but GHB results are closer to the empirical results. Also, it is inferred that results are affected by the failure envelope not real failure plane. Therefore, obtained shear strength parameters are not mobilized. Finally, a multivariable regressed relation is presented for determining the stresses on the failure plane.

• Advances in Energy Research
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• v.6 no.2
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.129.1-129.1
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• 2012

서포트 벡터 머신(Support Vector Machine, SVM)과 인공신경망 모형(Neural Network, NN)을 사용하여 태양 양성자 현상(Solar proton event, SPE)의 플럭스 세기를 예측해 보았다. 이번 연구에서는 1976년부터 2011년까지 10MeV이상의 에너지를 가진 입자가 10개 cm-1 sec-1 ster -1 이상 입사할 경우를 태양 양성자 현상으로 정의한 NOAA의 태양 고에너지 입자 리스트와 GOE위성의 X-ray 플레어 데이터를 사용하였다. 여기에서 C, M, X 등급의 플레어와 관련있는 178개 이벤트를 모델의 훈련을 위한 데이터(training data) 89개와 예측을 위한 데이터(prediction data) 89개로 구분하였다. 플러스 세기의 예측을 위하여, 우리는 로그 플레어 세기, 플레어 발생위치, Rise time(플레어 시작시간부터 최대값까지의 시간)을 모델 입력인자로 사용하였다. 그 결과 예측된 로그 플럭스 세기와 관측된 로그 플럭스 세기 사이의 상관계수는 SVM과 NN에서 각각 0.32와 0.39의 값을 얻었다. 또한 두 값 사이의 평균 제곱근 오차(Root mean square error)는 SVM에서 1.17, NN에서는 0.82로 나왔다. 예측된 플럭스 세기와 관측된 플럭스 세기의 차이를 계산해 본 결과, 오차 범위가 1이하인 경우가 SVM에서는 약 68%이고 NN에서는 약 80%의 분포를 보였다. 이러한 결과로부터 우리는 NN모델이 SVM모델보다 플럭스 세기를 잘 예측하는 것을 알 수 있었다.