• Journal of Korean Society for Atmospheric Environment
• /
• v.19 no.4
• /
• pp.387-396
• /
• 2003

A field study was conducted during the summer time of 2002 to determine compositions of volatile organic compounds (VOCs) emitted from vehicles and to develop source emission profiles that is applied to CMB model to estimate the source contribution of certain area. Source emission profile is widely used for the estimation of source contribution by the chemical mass balance model and have to be developed applicable for the target area of estimation. This study was aimed to develop source emission profile and estimation of source contribution of VOCs after application of the chemical mass balance (CMB) receptor model. After considering the emission inventory and other research results for the VOCs in Seoul, Korea, the sources like vehicle emission (tunnel), gas station (gasoline, diesel), solvent usage (painting operation, dry cleaning, graphic art), and gas fuels were selected for the major VOCs sources. Furthermore, ambient air samples were simultaneously collected from 09:00 to 11:00 for four days at eight different official air quality monitoring sites as receptors in Seoul during summer of 2001. Source samples were collected by canisters, and then about seventy volatile organic compounds were analyzed by gas chromatography with flame ionization detector (GC/FID). Based on both the developed source profiles and the database of the receptors, CMB model was intensively applied to estimate mass contribution of VOCs sources. Examining the source profile from the vehicle, the portion of alkanes of VOCs was highest, and then the portion of aromatics such toluene, m/p-xylene were followed. In case of gas fuel. they have their own components; the content of butane, propane, ethane was higher than any other component according to the fuel usage. The average of the source apportionment on VOCs for 8 sites showed that the major sources were vehicle emission and gas fuels. The vehicle emission source was revealed as having the highest contribution with an average of 49.6%, and followed by solvent with 21.3%, gas fuel with 16.1%, gasoline with 13.1%.

• Journal of Korean Society for Atmospheric Environment
• /
• v.19 no.6
• /
• pp.701-717
• /
• 2003

The objective of this study was to extensively estimate the air quality trends of the study area by surveying con-centration trends in months or seasons, after analyzing the mass concentration of PM-10 samples and the inorganic lements, ion, and total carbon in PM-10. Also, the study introduced to apply the PMF (Positive Matrix Factoriza-tion) model that is useful when absence of the source profile. Thus the model was thought to be suitable in Korea that often has few information about pollution sources. After obtaining results from the PMF modeling, the existing sources at the study area were qualitatively identified The PM-10 particles collected on quartz fiber filters by a PM-10 high-vol air sampler for 3 years (Mar. 1999∼Dec.2001) in Kyung Hee University. The 25 chemical species (Al, Mn, Ti, V, Cr, Fe, Ni, Cu, Zn, As, Se, Cd, Ba, Ce, Pb, Si, N $a^{#}$, N $H_4$$^{+}$, $K^{+}$, $Mg^{2+}$, $Ca^{2+}$, C $l^{[-10]}$ , N $O_3$$^{[-10]}$ , S $O_4$$^{2-}$, TC) were analyzed by ICP-AES, IC, and EA after executing proper pre - treatments of each sample filter. The PMF model was intensively applied to estimate the quantitative contribution of air pollution sources based on the chemical information (128 samples and 25 chemical species). Through a case study of the PMF modeling for the PM-10 aerosols. the total of 11 factors were determined. The multiple linear regression analysis between the observed PM-10 mass concentration and the estimated G matrix had been performed following the FPEAK test. Finally the regression analysis provided source profiles (scaled F matrix). So, 11 sources were qualitatively identified, such as secondary aerosol related source, soil related source, waste incineration source, field burning source, fossil fuel combustion source, industry related source, motor vehicle source, oil/coal combustion source, non-ferrous metal source, and aged sea- salt source, respectively.ively.y.

• Korean Journal of Soil Science and Fertilizer
• /
• v.45 no.4
• /
• pp.497-502
• /
• 2012

This study investigates the prediction of soil OM on Korean soils using the Visible-Near Infrared (Vis-NIR) spectroscopy. The ASD Field Spec Pro was used to acquire the reflectance of soil samples to visible to near-infrared radiation (350 to 2500 nm). A total of 503 soil samples from 61 Korean soil series were scanned using the instrument and OM was measured using the Walkley and Black method. For data analysis, the spectra were resampled from 500-2450 nm with 4 nm spacing and converted to the $1^{st}$ derivative of absorbance (log (1/R)). Partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil OM. Regression rules model estimates the target value by building conditional rules, and each rule contains a linear expression predicting OM from selected absorbance values. The regression rules model was shown to give a better prediction compared to PLSR. Although the prediction for Andisols had a larger error, soil order was not found to be useful in stratifying the prediction model. The stratification used by Cubist was mainly based on absorbance at wavelengths of 850 and 2320 nm, which corresponds to the organic absorption bands. These results showed that there could be more information on soil properties useful to classify or group OM data from Korean soils. In conclusion, this study shows it is possible to develop good prediction model of OM from Korean soils and provide data to reexamine the existing prediction models for more accurate prediction.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.20 no.2
• /
• pp.225-230
• /
• 2017

To generate mechanical movements in one-shot devices such as missiles and space launch vehicles, pyrotechnic mechanical device(PMD) such as pin pullers using pyrotechnic charge has been widely used. Reliability prediction of pin pullers is crucial to successfully execute target missions for the one-shot devices. Because the pin pullers require destructive tests to evaluate their reliability, one would need about 3,000 samples of success to guarantee a reliability of 99.9 % with a confidence level of 95 %. This paper suggests the application of a probit model using the charge amount as a functional parameter for estimation of functional reliability of pin puller. To guarantee target reliability, we propose estimation methods of the lower bound of functional reliability by applying the probit model. Given lower bound of functional reliability, we quantitatively show that the optimum amount of charge increases as the number of samples decreases. Along with a variety of simulations the validity of our new model via real test results is confirmed.

• Geomechanics and Engineering
• /
• v.20 no.1
• /
• pp.75-86
• /
• 2020

This study aims to simulate the stabilization process of fibrous peat samples using end-bearing Cement Deep Mixing (CDM) columns by three area improvement ratios of 13.1% (TS-2), 19.6% (TS-3) and 26.2% (TS-3). It also focuses on the determination of approximate stress distribution between CDM columns and untreated fibrous peat soil. First, fibrous peat samples were mechanically stabilized using CDM columns of different area improvement ratio. Further, the ultimate bearing capacity of a rectangular foundation rested on the stabilized peat was calculated in stress-controlled condition. Then, this process was simulated via a FEM-based model using Plaxis 3-D foundation and the numerical modelling results were compared with experimental findings. In the numerical modelling stage, the behaviour of fibrous peat was simulated based on hardening soil (HS) model and Mohr-Coulomb (MC) model, while embedded pile element was utilized for CDM columns. The results indicated that in case of untreated peat HS model could predict the behaviour of fibrous peat better than MC model. The comparison between experimental and numerical investigations showed that the stress distribution between soil (S) and CDM columns (C) were 81%C-19%S (TS-2), 83%C-17%S (TS-3) and 89%C-11%S (TS-4), respectively. This implies that when the area improvement ratio is increased, the share of the CDM columns from final load was increased. Finally, the calculated bearing capacity factors were compared with results on the account of empirical design methods.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.59 no.5
• /
• pp.93-99
• /
• 2017

A model was developed using Artificial Neural Networks (ANNs) based on Principal Component Analysis (PCA), to accurately predict the air humidity inside an experimental greenhouse located in Daegu (latitude $35.53^{\circ}N$, longitude $128.36^{\circ}E$, and altitude 48 m), South Korea. The weather parameters, air temperature, relative humidity, solar radiation, and carbon dioxide inside and outside the greenhouse were monitored and measured by mounted sensors. Through the PCA of the data samples, three main components were used as the input data, and the measured inside humidity was used as the output data for the ALYUDA forecaster software of the ANN model. The Nash-Sutcliff Model Efficiency Coefficient (NSE) was used to analyze the difference between the experimental and the simulated results, in order to determine the predictive power of the ANN software. The results obtained revealed the variables that affect the inside air humidity through a sensitivity analysis graph. The measured humidity agreed well with the predicted humidity, which signifies that the model has a very high accuracy and can be used for predictions based on the computed $R^2$ and NSE values for the training and validation samples.

• Proceedings of the Fisheries Business Administration Society of Korea Conference
• /
• 2007.12a
• /
• pp.167-184
• /
• 2007

The objectives of this paper are to identify the causes of the corporate distress and to develop a distress prediction model with the financial information in fishery industry. In this study, the corporate distress is defined as economic failure and technical insolvency. Economic failure occurs by reduction, shut-down, or change of the business and technical insolvency results from failure to pay the financial debt of companies. The 33 distressed firms from 1991 to 2003 were composed by 14 economic failure companies, 15 technical insolvency companies. 4 companies applied to the both cases. The analysis of distress prediction of fishery companies were accomplished according to the distress definition. The analysis was carried out as two steps. The first step was the univariate analysis, which was used for checking the prediction power of individual financial variable. The t-test is used to identify the differences in financial variables between the distressed group and the non-distressed group. The second step was to develop distress prediction model with logistic regression. The variables showed the significant difference in univariate analysis were selected as the prediction variables. The financial ratios, used in the logistic regression model, were selected by backward elimination method. To test stability of the distress prediction model, the whole sample was divided as three sub-samples, period 1(1990$\sim$1993), period 2(1994$\sim$1997), period 3(1998$\sim$2002). The final model built from whole sample appled each three sub-samples. The results of the logistic analysis were as follows. the growth, profitability, stability ratios showed the significant effect on the distress. the some different result was found in the sub-sample (economic failure and technical insolvency). The growth and the profitability were important to predict the economic failure. The profitability and the activity were important to predict technical insolvency. It means that profitability is the really important factor to the fishery companies.

• The Journal of Fisheries Business Administration
• /
• v.39 no.2
• /
• pp.61-79
• /
• 2008

The objectives of this paper are to identify the causes of the corporate distress and to develop a distress prediction model with the financial information in fishery industry. In this study, the corporate distress is defined as economic failure and technical insolvency. Economic failure occurs by reduction, shut - down, or change of the business and technical insolvency results from failure to pay the financial debt of companies. The 33 distressed firms from 1991 to 2003 were composed by 14 economic failure companies, 15 technical insolvency companies. 4 companies applied to the both cases. The analysis of distress prediction of fishery companies were accomplished according to the distress definition. The analysis was carried out as two steps. The first step was the univariate analysis, which was used for checking the prediction power of individual financial variable. The t - test is used to identify the differences in financial variables between the distressed group and the non - distressed group. The second step was to develop distress prediction model with logistic regression. The variables showed the significant difference in univariate analysis were selected as the prediction variables. The financial ratios, used in the logistic regression model, were selected by backward elimination method. To test stability of the distress prediction model, the whole sample was divided as three sub-samples, period 1(1990 - 1993), period 2(1994 - 1997), period 3(1998 - 2002). The final model built from whole sample appled each three sub - samples. The results of the logistic analysis were as follows. the growth, profitability, stability ratios showed the significant effect on the distress. the some different result was found in the sub - sample (economic failure and technical insolvency). The growth and the profitability were important to predict the economic failure. The profitability and the activity were important to predict technical insolvency. It means that profitability is the really important factor to the fishery companies.

• Journal of Microbiology
• /
• v.40 no.2
• /
• pp.119-124
• /
• 2002

Experiments for the characterization of inactivation were performed in a series of batch processes with the total coliform used as a general indicator organism based on the chlorine residuals as a disinfectant. The water samples were taken from the outlet of a settling basin in a conventional surface water treat- ment system that is provided with the raw water drawn from the mid-stream of the Han River, The inactivation of total coliform was experimentally analysed for the dose of disinfectants contact time, filtration and mixing intensity. The curves obtained from a series of batch processes were shaped with a general tailing-off and biphasic mode of inactivation, i.e. a sharp loss of bacterial viability within 15 min followed by an extended phase. In order to observe the effect of carry-over suspended solids on chlorine consumption and disinfection efficiency, the water samples were filtered, prior to inoculation with coliforms, with membranes of both 2.5$\mu$m and 11.0 $\mu$m pore size, and with a sand tilter of 1.0 mm in effective size and of 1.4 in uniformity coefficient. As far as the disinfection efficiency is concerned, there were no significant differences. The parameters estimated by the models of Chick-Wat-son, Hom and Selleck from our experimental data obtained within 120 min are: log(N/N$\_$0/)=-0.16CT with n=1, leg(N/N$\_$0/)=-0.71C$\^$0.87/ with n 1 for the Chick-Watson model, log (N/N$\_$0/)=-1.87C$\^$0.47/ T$\^$0.36/ for the Hom model, log (MHo)=-2.13log (1+CT/0.11) for the Selleck model. It is notable that among the models reviewed with regard to the experimental data obtained, the Selleck model appeared to most closely resemble the total coliform survival curve.

• Biomedical Science Letters
• /
• v.24 no.4
• /
• pp.418-425
• /
• 2018

Amyloid brain positron emission tomography (PET) images are visually and subjectively analyzed by the physician with a lot of time and effort to determine the ${\beta}$-Amyloid ($A{\beta}$) deposition. We designed a convolutional neural network (CNN) model that predicts the $A{\beta}$-positive and $A{\beta}$-negative status. We performed 18F-florbetaben (FBB) brain PET on controls and patients (n=176) with mild cognitive impairment and Alzheimer's Disease (AD). We classified brain PET images visually as per the on the brain amyloid plaque load score. We designed the visual geometry group (VGG16) model for the visual assessment of slice-based samples. To evaluate only the gray matter and not the white matter, gray matter masking (GMM) was applied to the slice-based standard samples. All the performance metrics were higher with GMM than without GMM (accuracy 92.39 vs. 89.60, sensitivity 87.93 vs. 85.76, and specificity 98.94 vs. 95.32). For the patient-based standard, all the performance metrics were almost the same (accuracy 89.78 vs. 89.21), lower (sensitivity 93.97 vs. 99.14), and higher (specificity 81.67 vs. 70.00). The area under curve with the VGG16 model that observed the gray matter region only was slightly higher than the model that observed the whole brain for both slice-based and patient-based decision processes. Amyloid brain PET images can be appropriately analyzed using the CNN model for predicting the $A{\beta}$-positive and $A{\beta}$-negative status.