• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.246-259
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• 2008

This study was to minimize the trial and error in the primary step of the C4I system(ATCIS) of the each army corps on the front line, and test the economy and efficiency was tested by reviewing related papers and the system characteristics of other countries. The relationship was researched by analyzing the collected survey data and survey data related to the user's requirement level such as the army standards, that is, commonality, timeliness, simplification, automaticity, field availability and viability, multi-stage security and interoperability, unification. The result showed that the C4I system was efficiently operated through the system reliability for the specification of the system and operation manual, maneuverability and security, adaptability of the war field and system support and management, and good education and training about system operation, and less system maintenance and supplementary element. As a result, the development plan confirmed that the continuous operator education and the construction of the maintenance, and the upgrade digitalization(C4ISR+D) with the korean characteristics based on IT of network systems, and system development of the measurement model of the operator performance must be continuously supplemented in the near future.

• Radiation Oncology Journal
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• v.11 no.1
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• pp.175-181
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• 1993

Radiosurgery requires integral procedure where special devices and computer systems are needed for localization, dose planning and treatment. The aim of this work is to verify the overall mechanical accuracy of our LINAC and develop dose calculation algorithm for LINAC radiosurgery. The alignment of treatment machine and the performance testing of the entire system were extensively carried out and the basic data such as percent depth dose, off-axis ratio and output factor were measured. A three dimensional treatment planning system for stereotactic radiosurgery has been developed. We used an IBM personal computer with C programming language (IBM personal system/2, Model 80386, IBM Co., USA) for calculating the dose distribution. As a result, deviations at isocenter on gantry and table rotation for our treatment machine were acceptable since they were less than 2 mm. According to the phantom experiments, the focusing isocenter were successful by the error of less than 2 mm. Finally, the mechanical accuracy of our three dimensional planning system was confirmed by film dosimetry in sphere phantom.

• Journal of KSNVE
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• v.10 no.6
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• pp.1059-1066
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• 2000

In this paper. a new indirect feedback active noise control (ANC) scheme barred on the fundamental frequency estimation is proposed for systems with a harmonic noise. When reference signals necessary for feedforward ANC configuration are difficult to obtain, the conventional ANC algorithms for multi-tonal noise do not measure the reference signals but generate them with the estimated frequencies.$^{(4)}$ However, the beating phenomena, in which certain frequency components of the noise vanish intermittently, may make the adaptive frequency estimation difficult. The confusion in the estimated frequencies due to the beating phenomena makes the generated reference signals worthless. The proposed algorithm consists of two parts. The first part is a reference generator using the fundamental frequency estimation and the second one is the conventional feedforward control. We propose the fundamental frequency estimation algorithm using decision rules. which is insensitive to the beating phenomena. In addition, the proposed fundamental frequency estimation algorithm has good tracking capability and lower variance of frequency estimation error than that of the conventional cascade ANF method.$^{(4)}$ We are also able to control all interested modes of the noise, even which cannot be estimated by the conventional frequency estimation method because of the poor S/N ratio. We verify the performance of the proposed ANC method through simulations for the measured cabin noise of a passenger ship and the measured time-varying engine booming noise of a passenger vehicle.

• Korean Journal of Food Science and Technology
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• v.35 no.6
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• pp.1053-1059
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• 2003

This study aimed to determine the amount of vitamin C from vegetable & fruit juice by high performance liquid chromatograhy (HPLC). Components for estimation of measurement uncertainty associated with the analysis of vitamin C, such as standard weight, purity, molecular weight, dilution of standard solution, calibration curve, recovery, and precision, were importantly applied. The estimation of uncertainty obtained with systematic and random error based on the GUM (Guide to the expression of uncertainty in measurement) and EURACHEM document with mathematical calculation and statistical analysis. The components, evaluated ty either Type A or Type B methods, were combined to produce an overall value of uncertainty known as the combined standard uncertainty. An expanded uncertainty was obtained by multiplying the combined standard uncertainty with a coverage factor (k) calculated from the effective degree of freedom. The content of vitamin C from vegetable and fruit juice was 27.53 mg/100g and the expanded uncertainty by multiplying by the coverage factor (k, 2.06) was 0.63 mg/100g at a 95% confidence level. It was concluded that the main sources were, in order of recovery and precision, weight and purity of the reference material, dilution of the standard solution, and calibration curve. Careful experiments on other higher uncertainties is further needed in addition to better personal proficiency in sample analysis in terms of accuracy and precision.

• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.435-452
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• 2016

Land Surface Temperature (LST) has been operationally retrieved from the Communication, Ocean, and Meteorological Satellite (COMS) data by the spilt-window method (CSW_v2.0) developed by Cho et al. (2015). Although the CSW_v2.0 retrieved the LST with a reasonable quality compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) LST data, it showed a relatively poor performance for the strong inversion and lapse rate conditions. To solve this problem, the LST retrieval algorithm (CSW_v2.0) was updated using the simulation results of radiative transfer model (MODTRAN 4.0) by considering the diurnal variations of air temperature. In general, the upgraded version, CSW_v3.0 showed a similar correlation coefficient between the prescribed LSTs and retrieved LSTs (0.99), the relatively smaller bias (from -0.03 K to-0.012 K) and the Root Mean Square Error (RMSE) (from 1.39 K to 1.138 K). Particularly, CSW_v3.0 improved the systematic problems of CSW_v2.0 that were encountered when temperature differences between LST and air temperature are very large and/or small (inversion layers and superadiabatic lapse rates), and when the brightness temperature differences and surface emissivity differences were large. The bias and RMSE of CSW_v2.0 were reduced by 10-30% in CSW_v3.0. The indirect validation results using the MODIS LST data showed that CSW_3.0 improved the retrieval accuracy of LST in terms of bias (from -0.629 K to -0.049 K) and RMSE (from 2.537 K to 2.502 K) compared to the CSW_v2.0.

• Journal of the Korean Society of Tobacco Science
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• v.20 no.2
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• pp.183-190
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• 1998

This study was conducted to analyze chemical components in flue-cured tobacco using near infrared spectroscopy(NIRS). Samples were collected in '96 and '97 crop year and were scanned in the wavelengths of 400 ～ 2500 nm by near infrared analyzer(NIRSystem Co., Model 6500). Calibration equations were developed and then analyzed flue-cured samples by NIRS. The standard error of calibration(SEC) and performance (SEP) of '96 crop year samples between NIRS and standard laboratory analysis(SLA) were 0.18% and 0.24% for nicotine, 1.60% and 1.77% for total sugar, 0.13% and 0.15% for total nitrogen, 0.58% and 0.68% for crude ash, 0.23% and 0.28% for ether extracts, and 0.09% and 0.08% for chlorine, respectively. The coefficient of determination($R^2$) of calibration and prediction samples between NIRS and SLA of '96 crop year samples was 0.94～0.99 and 0.83～0.97 depending on chemical components, respectively. The SEC and SEP of '97 crop year samples were similar to those of '96 crop year samples. The SEP of '97 crop year samples which were analyzed using '96 calibration equation was 0.32 % for nicotine, 2.72% for total sugar, 0.14 % for total nitrogen, 1.00 % for crude ash, 0.48 for ether extracts and 0.17% for chlorine, respectively. The prediction result was more accurate when calibration and prediction samples were produced in the same crop year than those of the different crop year. The SEP of '96 and '97 crop year samples using calibration equation which was developed '96 plus '97 crop year samples was similar to that of '96 crop year samples using 96 calibration equation and that of '97 crop year samples using '97 calibration equation, respectively. The SEP of '97 crop year samples using calibration equation which was developed '96 plus '97 crop year samples was lower than that of '97 crop year samples analyzed by '96 calibration equation. To improve the analytical inaccuracy caused by the difference of crop year between calibration and prediction samples, we need to include the prediction sample spectra which are different from calibration sample spectra in recalibration sample spectra, and then develop recalibration equation. Although the analytical result using NIR is not as good as SLA, the chemical component analysis using NIR can apply to tobacco leaves, leaf process or tobacco manufacturing process which demand the rapid analytical result.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Journal of Biosystems Engineering
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• v.42 no.4
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• pp.314-322
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• 2017

Purpose: This study was conducted to investigate the hot air drying characteristics of squash slices depending on the drying conditions (input air velocity, input air temperature, and sample thickness). Methods: The developed drying system was equipped with a controllable air blower and electric finned heater, drying chamber, and ventilation fan. Squash (summer squash called Korean zucchini) samples were cut into slices of two different thicknesses (5 and 10 mm). These were then dried at two different input air temperatures (60 and $70^{\circ}C$) and air velocities (5 and 7 m/s). Six well-known drying models were tested to describe the experimental drying data. A non-linear regression analysis was applied to determine model constants and statistical indices such as the coefficient of determination ($R^2$), reduced chi-square (${\chi}^2$), and root mean square error (RMSE). In addition, the effective moisture diffusivity ($D_{eff}$) was estimated based on the curve of ln(MR) versus drying time. Results: The results clearly showed that drying time decreased with an increase in input air temperature. Slice thickness also affected the drying time. Air velocity had a greater influence on drying time at $70^{\circ}C$ than at $60^{\circ}C$ for both thicknesses. All drying models accurately described the drying curve of squash slices regardless of slice thickness and drying conditions; the Modified Henderson and Pabis model had the best performance with the highest R2 and the lowest RMSE values. The effective moisture diffusivity ($D_{eff}$) changes, obtained from Fick's diffusion method, were between $1.67{\times}10^{-10}$ and $7.01{\times}10^{-10}m^2/s$. The moisture diffusivity was increased with an increase in input air temperature, velocity, and thickness. Conclusions: The drying time of squash slices varied depending on input temperature, velocity, and thickness of slices. The further study is necessary to figure out optimal drying condition for squash slices with retaining its original quality.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.4 no.4
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• pp.335-344
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• 2006

In this paper a computer program was developed, which simulates the Li reduction process of PWR spent fuel, and the amount of a produced metal or chloride compound was calculated at the various amount of Li with the program. It establishes a database, which is composed of some characteristics related to a chemical reaction equation and thermodynamic data, and it calculates the transformed rate of PWR spent fuel oxide at the certain amount of Li by using the database as input data. As the results of the performance test of the program, it was validated that the transformed values of oxides, except for $Eu_2O_3$ and $Sm_2O_3$, were almost the same to within about a 6 % error with those calculated by the previous code and that the calculated amount of Li was also exactly consistent with the theoretical one, which is used for a complete reaction of each oxide in a single chemical reaction. A relationship between Li and the transformed metal of each oxide was analyzed on the basis of the quantities calculated with the verified development program. Of the results, when the amount of Li was given to be 250 mole, the 83.73 percentage of $UO_2$ was transformed into U while the remainder was still to be $UO_2$. In addition, it was appeared that the 297 mole of Li was needed to completely convert $UO_2$ into U.

• Ecology and Resilient Infrastructure
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• v.7 no.2
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• pp.114-125
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• 2020

Despite the various artificial neural networks that have been developed, most of the discharge models in previous studies have been developed using deep neural networks. This study aimed to develop a discharge model using a convolution neural network (CNN), which was used to solve classification problems. Furthermore, the applicability of CNN was evaluated. The photographs (pictures or images) for input data to CNN could not clearly show the characteristics of the study area as well as precipitation. Hence, the model employed in this study had to use numerical images. To solve the problem, the CN of NRCS was used to generate images as input data for the model. The generated images showed a good possibility of applicability as input data. Moreover, a new application of CN, which had been used only for discharge prediction, was proposed in this study. As a result of CNN training, the model was trained and generalized stably. Comparison between the actual and predicted values had an R² of 0.79, which was relatively high. The model showed good performance in terms of the Pearson correlation coefficient (0.84), the Nash-Sutcliffe efficiency (NSE) (0.63), and the root mean square error (24.54 ㎥/s).