• Near Infrared Analysis
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• v.1 no.1
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• pp.1-7
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• 2000

A convenient algorithm for optimizing wavelength selection in multiple linear regression (MLR) has been developed. MOP (MLP Optimization Program) has been developed to test all possible MLR calibration models in a given spectral range and finally find an optimal MLR model with external validation capability. MOP generates all calibration models from all possible combinations of wavelength, and simultaneously calculates SEC (Standard Error of Calibration) and SEV (Standard Error of Validation) by predicting samples in a validation data set. Finally, with determined SEC and SEV, it calculates another parameter called SAD (Sum of SEC, SEV, and Absolute Difference between SEC and SEV: sum(SEC+SEV+Abs(SEC-SEV)). SAD is an useful parameter to find an optimal calibration model without over-fitting by simultaneously evaluating SEC, SEV, and difference of error between calibration and validation. The calibration model corresponding to the smallest SAD value is chosen as an optimum because the errors in both calibration and validation are minimal as well as similar in scale. To evaluate the capability of MOP, the determination of benzene content in unleaded gasoline has been examined. MOP successfully found the optimal calibration model and showed the better calibration and independent prediction performance compared to conventional MLR calibration.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.185-195
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• 2009

As most systems get more complicated, system analysis using simulation has been taken notice of. One of the core parts of simulation analysis is validation of a simulation model, and we can identify how well the simulation model represents the real system with this validation process. The difference between input data of two systems has an effect on the comparison between a simulation model and a real system at validation stage, and the result with such difference is not enough to ensure high credibility of the model. Accordingly, in this paper, we construct a model based on Trace-driven simulation which uses identical input data with the real system. On the other hand, to validate a model by each class, not by an unique statistic, we validate the model using a metric transformed from F-measure which estimates performance of a classifier in data mining field. Finally, this procedure enables precise validation process of a model, and it helps modification by offering feedback at the validation phase.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.11
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• pp.485-491
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• 2006

In this paper, we study the problem of model selection for Support Vector Machine(SVM) predictor for short-term load forecasting. The model selection amounts to tuning SVM parameters, such as the cost coefficient C and kernel parameters and so on, in order to maximize the prediction performance of SVM. We propose that Cross-Validation method can be used as a model selection algorithm for SVM-based load forecasting technique. Through the various experiments on several data sets, we found that the difference between the prediction error of SVM using Cross-Validation and that of ideal SVM is less than 5%. This shows that SVM parameters for load forecasting can be efficiently tuned by using Cross-Validation.

• Proceedings of the KSRS Conference
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• v.1
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• pp.394-397
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• 2006

Gridded products of surface wind/wind-stress over the world ocean have been constructed by using satellite scatterometer as the Japanese Ocean Flux data sets with Use of Remote-sensing Observation (J-OFURO) data. Our previous validation study in the tropical Pacific using TAO/Triton and NDBC buoys revealed high reliability of our products. In this study, the Kuroshio Extension Observatory (KEO) buoy data are used for validation of other gridded wind-stress products including the NCEP-1 and 2 in the western North Pacific region where there have been few in-situ data. Results reveal that our J-OFURO product has almost zero mean difference and smallest root-mean-square (RMS) difference, while the NCEP-1 and 2 ones significantly positive biases and relatively high RMS difference. Intercomparison between the J-OFURO and NCEP products in a wide region of the North Pacific covered by the westerly winds exhibits that the NCEPs have larger magnitudes in the wind stress than the J-OFURO's, suggesting overestimation of the NCEPs.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.395-409
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• 2022

The purpose of this study is to evaluate the classification performance and applicability when land cover datasets constructed for AI training are cross validation to other areas. For study areas, Gyeongsang-do and Jeolla-do in South Korea were selected as cross validation areas, and training datasets were obtained from AI-Hub. The obtained datasets were applied to the U-Net algorithm, a semantic segmentation algorithm, for each region, and the accuracy was evaluated by applying them to the same and other test areas. There was a difference of about 13-15% in overall classification accuracy between the same and other areas. For rice field, fields and buildings, higher accuracy was shown in the Jeolla-do test areas. For roads, higher accuracy was shown in the Gyeongsang-do test areas. In terms of the difference in accuracy by weight, the result of applying the weights of Gyeongsang-do showed high accuracy for forests, while that of applying the weights of Jeolla-do showed high accuracy for dry fields. The result of land cover classification, it was found that there is a difference in classification performance of existing datasets depending on area. When constructing land cover map for AI training, it is expected that higher quality datasets can be constructed by reflecting the characteristics of various areas. This study is highly scalable from two perspectives. First, it is to apply satellite images to AI study and to the field of land cover. Second, it is expanded based on satellite images and it is possible to use a large scale area and difficult to access.

• Journal of Astronomy and Space Sciences
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• v.28 no.1
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• pp.63-70
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• 2011

The needs for satellite formation flying are gradually increasing to perform the advanced space missions in remote sensing and observation of the space or Earth. Formation flying in low Earth orbit can perform the scientific missions that cannot be realized with a single spacecraft. One of the various techniques of satellite formation flying is the determination of the precise baselines between the satellites within the formation, which has to be in company with the precision validation. In this paper, the baseline of Gravity Recovery and Climate Experiment (GRACE) A and B was determined with the real global positioning system (GPS) measurements of GRACE satellites. And baseline precision was validated with the batch and sequential processing methods using K/Ka-band ranging system (KBR) biased range measurements. Because the proposed sequential method validate the baseline precision, removing the KBR bias with the epoch difference instead of its estimation, the validating data (KBR biased range) are independent of the data validated (GPS-baseline) and this method can be applied to the real-time precision validation. The result of sequential precision validation was 1.5~3.0 mm which is similar to the batch precision validation.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.104-119
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• 2022

Life safety awareness level diagnosis is necessary for customized safety education and continuous safety awareness. As the starting stage of safety education for each life cycle, a scale that has verified the reliability and validity of high school students' life safety awareness has not yet been developed. In this context, the purpose of this study is to develop and validate the life safety awareness scale of high school students and to analyze interindividual differences. Questionnaire data was collected from April to June 2022 from 834 students in the first, second, and third grades of high schools in △△ city in Jeollabuk-do. A final 25-item scale was developed using the preliminary survey, preliminary test, the main test, descriptive statistical analysis, and exploratory and confirmatory factor analysis. This scale consists of four sub-factors: 'safety prevention', 'safety knowledge', 'safety preparation', and 'safety protection'. Good reliability and validity were verified by analysis of content validity and construct validity. The generalizability of the scale was verified by crossover validation between the search group and the crossover group. Based on the interindividual differences analysis, although there was a difference between genders in life safety awareness, there was no difference by grade level and academic achievement. This study is significant in developing the first valid scale that can measure high school students' life safety awareness and providing the necessity and rationale for life safety education by life cycle considering individual gender differences.

• Environmental Engineering Research
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• v.23 no.4
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• pp.442-448
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• 2018

This paper presents the numerical simulation of advection-diffusion mechanism of BOD concentration which was used as an indicator of waste only in one flow-direction of waste stabilization ponds (1-dimension (1-D)). This model was represented in partial differential equation order 2. The purpose of this paper was to determine the simulation of the model 1-D of wastewater transport phenomena based advection-diffusion mechanism and did validate the model. Numerical methods which was used for the solution of this model is finite difference method with Forward Time Central Space scheme. The simulation results which was obtained would be compared with field observation data as a validation model. Collection of field data was carried out in the Wastewater Treatment Plant Sewon, Bantul, D.I. Yogyakarta. The results of numerical simulations were indicate that the advection-diffusion mechanism takes place continuously over time. Then validation of the model was state that there was a difference between the calculation results with the field data, with a correlation value of 0.998.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2151-2161
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• 2021

Nuclear data (ND) sensitivity and uncertainty (S/U) quantification in shielding applications is performed using deterministic and probabilistic approaches. In this paper the validation of the newly developed deterministic program package ASUSD (ADVANTG + SUSD3D) is presented. ASUSD was developed with the aim of automating the process of ND S/U while retaining the computational efficiency of the deterministic approach to ND S/U analysis. The paper includes a detailed description of each of the programs contained within ASUSD, the computational workflow and validation results. ASUSD was validated on two shielding benchmark experiments from the Shielding Integral Benchmark Archive and Database (SINBAD) - the fission relevant ASPIS Iron 88 experiment and the fusion relevant Frascati Neutron Generator (FNG) Helium Cooled Pebble Bed (HCPB) Test Blanket Module (TBM) mock-up experiment. The validation process was performed in two stages. Firstly, the Denovo discrete ordinates transport solver was validated as a standalone solver. Secondly, the ASUSD program package as a whole was validated as a ND S/U analysis tool. Both stages of the validation process yielded excellent results, with a maximum difference of 17% in final uncertainties due to ND between ASUSD and the stochastic ND S/U approach. Based on these results, ASUSD has proven to be a user friendly and computationally efficient tool for deterministic ND S/U analysis of shielding geometries.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.446-448
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• 2007

This paper presents the high frequency (HF) eigenvalue results against IEEE SSR First Benchmark Mode and validation using the simultaneous time-domain simulation program, PSCAD/EMTDC. Two results show a little difference but not much. Particularly, HF eigenvalue analysis results tends to give more conservative results compared to those of the exact time-domain simulation.