• Journal of the Society of Naval Architects of Korea
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• v.28 no.2
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• pp.159-173
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• 1991

This paper describes a potential-based panel method formulated for the analysis of a super-cavitating two-dimensional hydrofoil. The method employs normal dipoles and sources distributed on the foil and cavity surfaces to represent the potential flow around the cavitating hydrofoil. The kinematic boundary condition on the wetted portion of the foil surface is satisfied by requiring that the total potential vanish in the fictitious inner flow region of the foil, and the dynamic boundary condition on the cavity surface is satisfied by requiring thats the potential vary linearly, i.e., the tangential velocity be constant. Green's theorem then results in a potential-based integral equation rather than the usual velocity-based formulation of Hess & Smith type. With the singularities distributed on the exact hydrofoil surface, the pressure distributions are predicted with improved accuracy compared to those of the linearized lilting surface theory, especially near the leading edge. The theory then predicts the cavity shape and cavitation number for an assumed cavity length. To improve the accuracy, the sources and dipoles on the cavity surface are moved to the newly computed cavity surface, where the boundary conditions are satisfied again. This iteration process is repeated until the results are converged. Characteristics of iteration and discretization of the present numerical method are much faster and more stable than the existing nonlinear theories. The theory shows good correlations with the existing theories and experimental results for the super-cavitating flow. In the region of small angles of attack, the present prediction shows and excellent comparison with the Geurst's linear theory. For the long cavity, the method recovers the trends of the Wu's nonlinear theory. In the intermediate regions of the short super-cavitation, the method compares very well with the experimental results of Parkin and also those of Silberman.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Analytical Science and Technology
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• v.19 no.1
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• pp.107-114
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• 2006

Early diagnosis and medical intervention are critical for the treatment of patients with metabolic disorders. A rapid analytical method was developed for simultaneous quantification of organic acids and amino acids in urine without labor-intensive pre-extraction procedure showing high sensitivity and specificity. A new method consisted of simple two-step trimethylsilyl (TMS)-trifluoroacetyl (TFA) derivatization using GC/MS-selective ion monitoring (SIM). Filter paper urine specimens were dried under nitrogen after being fortified with internal standard (tropate) in a mixture of distilled water and methanol. Methyl orange was added to the residue as indicator reagent. Silyl derivative of carboxylic functional group was followed by trifluoroacetyl derivative for amino functional group. N-methyl-N-(trimethylsilyl-trifluoroacetamide) and N-methyl-bistrifluoroacetamide were consecutively added and heated for 15-20 min at $65^{\circ}C-70^{\circ}C$, for TMS-TFA derivative, respectively. This reactant was analyzed by GC/MS-SIM. Linear dynamic range showed 0.001-50 mg with the detection limit of (S/N=3) 10-200 ng, and the quantification limit of 80-900 ng in urine. Correlation coefficient of regression line was 0.994-0.998. When the method was applied to the patients 'urine, it clearly differentiated the normal from the patient with metabolic disorder. The study showed that the developed method could be the method of choices in rapid and sensitive screening for organic aciduria and amino acidopathy.

• The Journal of Korean Society for Radiation Therapy
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• v.24 no.1
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• pp.1-10
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• 2012

Purpose: To evaluate dose distribution differences when the dose rates are randomly changed in intensity-modulated radiation therapy using a dynamic multileafcollimator. Materials and Methods: Two IMRT treatment plans including small-field and large-field plans were made using a commercial treatment planning system (Eclipse, Varian, Palo Alto, CA). Each plan had three sub-plans according to various dose rates of 100, 400, and 600 MU/min. A chamber array (2D-Array Seven729, PTW-Freiburg) was positioned between solid water phantom slabs to give measurement depth of 5 cm and backscattering depth of 5 cm. Beam deliveries were performed on the array detector using a 6 MV beam of a linear accelerator (Clinac 21EX, Varian, Palo Alto, CA) equipped with 120-leaf MLC (Millenium 120, Varian). At first, the beam was delivered with same dose rates as planned to obtain reference values. After the standard measurements, dose rates were then changed as follows: 1) for plans with 100 MU/min, dose rate was varied to 200, 300, 400, 500 and 600 MU/min, 2) for plans with 400 MU/min, dose rate was varied to 100, 200, 300, 500 and 600 MU/min, 3) for plans with 600 MU/min, dose rate was varied to 100, 200, 300, 400 and 500 MU/min. Finally, using an analysis software (Verisoft 3.1, PTW-Freiburg), the dose difference and distribution between the reference and dose-rate-varied measurements was evaluated. Results: For the small field plan, the local dose differences were -0.8, -1.1, -1.3, -1.5, and -1.6% for the dose rate of 200, 300, 400, 500, 600 MU/min, respectively (for 100 MU/min reference), +0.9, +0.3, +0.1, -0.2, and -0.2% for the dose rate of 100, 200, 300, 500, 600 MU/min, respectively (for 400 MU/min reference) and +1.4, +0.8, +0.5, +0.3, and +0.2% for the dose rate of 100, 200, 300, 400, 500 MU/min, respectively (for 600 MU/min reference). On the other hand, for the large field plan, the pass-rate differences were -1.3, -1.6, -1.8, -2.0, and -2.4% for the dose rate of 200, 300, 400, 500, 600 MU/min, respectively (for 100 MU/min reference), +2.0, +1.8, +0.5, -1.2, and -1.6% for the dose rate of 100, 200, 300, 500, 600 MU/min, respectively (for 400 MU/min reference) and +1.5, +1.9, +1.7, +1.9, and +1.2% for the dose rate of 100, 200, 300, 400, 500 MU/min, respectively (for 600 MU/min reference). In short, the dose difference of dose-rate variation was measured to the -2.4~+2.0%. Conclusion: Using the Varian linear accelerator with 120 MLC, the IMRT dose distribution is differed a little <(${\pm}3%$) even though the dose-rate is changed.

• Journal of Korean Society of Steel Construction
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• v.22 no.1
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• pp.1-12
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• 2010

According to the capacity design concept which forms the basis of the current steel seismic codes, the braces in concentrically braced frames (CBFs) should dissipate seismic energy through cyclic tension yielding and cyclic compression buckling while the beams and the columns should remain elastic. Brace buckling in inverted V-braced frames induces unbalanced vertical forces which, in turn, impose the additional beam moments and column axial forces. However, due to difficulty in predicting the location of buckling stories, the most conservative approach implied in the design code is to estimate the column axial forces by adding all the unbalanced vertical forces in the upper stories. One alternative approach, less conservative and recommended by the current code, is to estimate the column axial forces based on the amplified seismic load expected at the mechanism-level response. Both are either too conservative or lacking technical foundation. In this paper, three combination rules for a rational estimation of the column axial forces were proposed. The idea central to the three methods is to detect the stories of high buckling potential based on pushover analysis and dynamic behavior. The unbalanced vertical forces in the stories detected as high buckling potential are summed in a linear manner while those in other stories are combined by following the SRSS(square root of sum of squares) rule. The accuracy and design advantage of the three methods were validated by comparing extensive inelastic dynamic analysis results. The mode-shape based method(MSBM), which is both simple and accurate, is recommended as the method of choice for practicing engineers among the three.

• Korean Journal of Biological Psychiatry
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• v.5 no.1
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• pp.95-101
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• 1998

The changes of electroencephalogram(EEG) in patients with dementia of Alzheimer's type are most commonly studied by analyzing power or magnitude in traditionally defined frequency bands. However because of the absence of an identified metric which quantifies the complex amount of information, there are many limitations in using such a linear method. According to the chaos theory, irregular signals of EEG can be also resulted from low dimensional deterministic chaos. Chaotic nonlinear dynamics in the EEG can be studied by calculating the largest Lyapunov exponent($L_1$). The authors have analyzed EEG epochs from three patients with dementia of Alzheimer's type and three matched control subjects. The largest $L_1$ is calculated from EEG epochs consisting of 16,384 data points per channel in 15 channels. The results showed that patients with dementia of Alzheimer's type had significantly lower $L_1$ than non-demented controls on 8 channels. Topographic analysis showed that the $L_1$ were significantly lower in patients with Alzheimer's disease on all the frontal, temporal, central, and occipital head regions. These results show that brains of patients with dementia of Alzheimer's type have a decreased chaotic quality of electrophysiological behavior. We conclude that the nonlinear analysis such as calculating the $L_1$ can be a promising tool for detecting relative changes in the complexity of brain dynamics.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.243-248
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• 2017

Sea ice which is an important component of the global climate system is being actively detected by satellite because it have been distributed to polar and high-latitude region. and the sea ice detection method using satellite uses reflectance and temperature data. the sea ice detection method of Moderate-Resolution Imaging Spectroradiometer (MODIS), which is a technique utilizing Ice Surface Temperature (IST) have been utilized by many studies. In this study, we propose a simple and effective method of sea ice detection using the dynamic threshold technique with no IST calculation process. In order to specify the dynamic threshold, pixels with freezing point of MODIS IST of 273.0 K or less were extracted. For the extracted pixels, we analyzed the relationship between MODIS IST, MODIS $11{\mu}m$ channel brightness temperature($T_{11{\mu}m}$) and Brightness Temperature Difference ($BTD:T_{11{\mu}m}-T_{12{\mu}m}$). As a result of the analysis, the relationship between the three values showed a linear characteristic and the threshold value was designated by using this. In the case ofsea ice detection, if $T_{11{\mu}m}$ is below the specified threshold value, it is detected as sea ice on clear sky. And in order to estimate the performance of the proposed sea ice detection method, the accuracy was analyzed using MODIS Sea ice extent and then validation accuracy was higher than 99% in Producer Accuracy (PA).

• Progress in Medical Physics
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• v.16 no.2
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• pp.70-76
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• 2005

For clinical implementation of Enhanced Dynamic Wedge (EDW), it is necessary to adequately analyze and commission its dosimetric properties in comparison to common physical metal wedge (MTW). This study was implemented with the essential measurements of parameters for clinical application, such as percentage depth dose, peripheral dose, surface dose, effective wedge factor, and wedge profile. In addition, through the comparison study of EDW with open and MTW, the analysis was performed to characterize the EDW. We also compared EDW dose profiles of measured values using chamber array 24 (CA24) with calculated values using radiation treatment planning system. PDDs of EDW showed good agreements between $0.2\~0.5\%$ of open beam, but $2\%$ differences with MTW. In the result of the measurements of peripheral dose, it was shown that MTW was about $1\%$ higher than open field and EDW. The surface doses of $60^{\circ}$ MTW showed 10% lower than the others. We found that effective wedge factor of EDW had linear relationships according to Y jaw sizes and was independent of X jaw sizes and was independent of X jaw sizes and asymmetric Y jaw opening. In comparison with measured values and calculate values from Golden-STT based radiation treatment planning system (RTP system), it showed very good agreement within difference of $1\%$. It could be concluded that EDW is a very reliable and useful tool as a beam modification substitute for conventional MTW.

• Asia pacific journal of information systems
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• v.23 no.3
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• pp.25-53
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• 2013

This study analyzes the effect of Computer Aided Innovation (CAI) to improve R&D Capabilities empirically. Survey was distributed by e-mail and Google Docs, targeting CTO of 235 SMEs. 142 surveys were returned back (rate of return 60.4%) from companies. Survey results from 119 companies (83.8%) which are effective samples except no-response, insincere response, estimated value, etc. were used for statistics analysis. Companies with less than 50billion KRW sales of entire researched companies occupy 76.5% in terms of sample traits. Companies with less than 300 employees occupy 83.2%. In terms of the type of company business Partners (called 'partners with big companies' hereunder) who work with big companies for business occupy 68.1%. SMEs based on their own business (called 'independent small companies') appear to occupy 31.9%. The present status of holding IT system according to traits of company business was classified into partners with big companies versus independent SMEs. The present status of ERP is 18.5% to 34.5%. QMS is 11.8% to 9.2%. And PLM (Product Life-cycle Management) is 6.7% to 2.5%. The holding of 3D CAD is 47.1% to 21%. IT system-holding and its application of independent SMEs seemed very vulnerable, compared with partner companies of big companies. This study is comprised of IT infra and IT Utilization as CAI capacity factors which are independent variables. factors of R&D capabilities which are independent variables are organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability. The highest average value of variables was 4.24 in organization capability 2. The lowest average value was 3.01 in IT infra which makes users access to data and information in other areas and use them with ease when required during new product development. It seems that the inferior environment of IT infra of general SMEs is reflected in CAI itself. In order to review the validity used to measure variables, Factors have been analyzed. 7 factors which have over 1.0 pure value of their dependent and independent variables were extracted. These factors appear to explain 71.167% in total of total variances. From the result of factor analysis about measurable variables in this study, reliability of each item was checked by Cronbach's Alpha coefficient. All measurable factors at least over 0.611 seemed to acquire reliability. Next, correlation has been done to explain certain phenomenon by correlation analysis between variables. As R&D capabilities factors which are arranged as dependent variables, organization capability, process capability, HR capability, technology-accumulating capability, and internal/external collaboration capability turned out that they acquire significant correlation at 99% reliability level in all variables of IT infra and IT Utilization which are independent variables. In addition, correlation coefficient between each factor is less than 0.8, which proves that the validity of this study judgement has been acquired. The pair with the highest coefficient had 0.628 for IT utilization and technology-accumulating capability. Regression model which can estimate independent variables was used in this study under the hypothesis that there is linear relation between independent variables and dependent variables so as to identify CAI capability's impact factors on R&D. The total explanations of IT infra among CAI capability for independent variables such as organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability are 10.3%, 7%, 11.9%, 30.9%, and 10.5% respectively. IT Utilization exposes comprehensively low explanatory capability with 12.4%, 5.9%, 11.1%, 38.9%, and 13.4% for organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability respectively. However, both factors of independent variables expose very high explanatory capability relatively for technology-accumulating capability among independent variable. Regression formula which is comprised of independent variables and dependent variables are all significant (P<0.005). The suitability of regression model seems high. When the results of test for dependent variables and independent variables are estimated, the hypothesis of 10 different factors appeared all significant in regression analysis model coefficient (P<0.01) which is estimated to affect in the hypothesis. As a result of liner regression analysis between two independent variables drawn by influence factor analysis for R&D capability and R&D capability. IT infra and IT Utilization which are CAI capability factors has positive correlation to organization capability, process capability, human resources capability, technology-accumulating capability, and collaboration capability with inside and outside which are dependent variables, R&D capability factors. It was identified as a significant factor which affects R&D capability. However, considering adjustable variables, a big gap is found, compared to entire company. First of all, in case of partner companies with big companies, in IT infra as CAI capability, organization capability, process capability, human resources capability, and technology capability out of R&D capacities seems to have positive correlation. However, collaboration capability appeared insignificance. IT utilization which is a CAI capability factor seemed to have positive relation to organization capability, process capability, human resources capability, and internal/external collaboration capability just as those of entire companies. Next, by analyzing independent types of SMEs as an adjustable variable, very different results were found from those of entire companies or partner companies with big companies. First of all, all factors in IT infra except technology-accumulating capability were rejected. IT utilization was rejected except technology-accumulating capability and collaboration capability. Comprehending the above adjustable variables, the following results were drawn in this study. First, in case of big companies or partner companies with big companies, IT infra and IT utilization affect improving R&D Capabilities positively. It was because most of big companies encourage innovation by using IT utilization and IT infra building over certain level to their partner companies. Second, in all companies, IT infra and IT utilization as CAI capability affect improving technology-accumulating capability positively at least as R&D capability factor. The most of factor explanation is low at around 10%. However, technology-accumulating capability is rather high around 25.6% to 38.4%. It was found that CAI capability contributes to technology-accumulating capability highly. Companies shouldn't consider IT infra and IT utilization as a simple product developing tool in R&D section. However, they have to consider to use them as a management innovating strategy tool which proceeds entire-company management innovation centered in new product development. Not only the improvement of technology-accumulating capability in department of R&D. Centered in new product development, it has to be used as original management innovative strategy which proceeds entire company management innovation. It suggests that it can be a method to improve technology-accumulating capability in R&D section and Dynamic capability to acquire sustainable competitive advantage.

• Environmental and Resource Economics Review
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• v.13 no.4
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• pp.593-619
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• 2004

This study analyzes the interfuel substitution of energy demand in Korean manufacturing sector using static and dynamic linear logit models. For the period of 1981~2002, this study uses petroleum, electricity, natural gas and coal as energy sources. According to the empirical results, firstly, the own-price elasticity of coal has been increased steadily even though its elasticity is smallest compared with those of other energy sources. On the other hand, price elasticity of natural gas is largest, but its value has been decreased after 1997. Price elasticities of petroleum and electricity are very stable over the sample period. One of the main features in trends of interfuel substitution is as follows. Substitution effect of a change in price of natural gas on both petroleum and coal has been increased especially after 1997. The implication of the empirical results is summarized as follows: First, the fact of inelastic own-price elasticity of petroleum implies that the dependency of Korean manufacturing sector on petroleum and coal will be persistent even in a sharp fluctuation of petroleum price. Second, the effects of price increase in natural gas on demand for petroleum and coal are very significant. Thus, price decline of natural gas rather than price declines of coal and petroleum could be more effective as an energy price policy for the reduction of $CO_2$ emission. The assessment on this implication will remain for future researches.