• Journal of the Society of Naval Architects of Korea
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• v.28 no.2
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• pp.40-51
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• 1991

A Propeller design method using the newly developed blade section(KH18), which behaves better cavitation characteristics, is presented. Experimental results for two-dimensional foil sections show that the lift-drag curve and the cavitation-free bucket diagram of the new blade section are wider comparing to those of the existion NACA sections. This characteristic of the new section is particularly important for marine propeller applications since angle of attack variation of the propeller blade operating behind a non-uniform ship's wake is relatively large. A lifting surface theory is used for the design of a propeller with the developed section for a 2700 TEU container ship. Since the most suitable chordwise loading shape is not known a priori, chordwise loading shape is chosen as a design parameter. Five propellers with different chordwise loading shapes and different foil sections are designed and tested in the towing tank and cavitation tunnel at KRISO. It is observed by a series of extensive model tsets that the propeller(KP197) having the chordwise loading shape, which has less leading edge loading at the inner radii and more leading edge loading at the outer radii of 0.7 radius, has higher propulsive efficiency and better cavitation characteristics. The KP197 propeller shows 1% higher efficiency, 30% cavitation volume reduction and 9% reduction of fluctuating pressure level comparing to the propeller with an NACA section. More appreciable efficiency gain for the new blade section propeller would be expected by reduction of expanded blade area considering the better cavitation characteristics of the new blade section.

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.325-340
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• 2008

This study employs Bayesian multiple regression analysis using the ordinary least squares method for regional low flow frequency analysis. The parameter estimates using the Bayesian multiple regression analysis were compared to conventional analysis using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian analysis at each return period are not significantly different. However, the difference between upper and lower limits is remarkably reduced using the Bayesian multiple regression. Therefore, from the point of view of uncertainty analysis, Bayesian multiple regression analysis is more attractive than the conventional method based on a t-distribution because the low flow sample size at the site of interest is typically insufficient to perform low flow frequency analysis. Also, we performed low flow prediction, including confidence interval, at two ungauged catchments in the Nakdong River basin using the developed Bayesian multiple regression model. The Bayesian prediction proves effective to infer the low flow characteristic at the ungauged catchment.

• Journal of the Korea Concrete Institute
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• v.14 no.1
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• pp.118-125
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• 2002

Hydration is the main reason for the growth of the material properties. An exact parameter to control the chemical and physical process is not the time, but the degree of hydration. Therefore, it is reasonable that development of all material properties and the formation of microstructure should be formulated in terms of degree of hydration. Mathematical formulation of degree of hydration is based on combination of reaction rate functions. The effect of moisture conditions as well as temperature on the rate of reaction is considered in the degree of hydration model. This effect is subdivided into two contributions: water shortage and water distribution. The former is associated with the effect of W/C ratio on the progress of hydration. The water needed for progress of hydration do not exist and there is not enough space for the reaction products to form. The tatter is associated with the effect of free capillary water distribution in the pore system. Physically absorption layer does not contribute to progress of hydration and only free water is available for further hydration. In this study, the effects of chemical composition of cement, W/C ratio, temperature, and moisture conditions on the degree of hydration are considered. Parameters that can be used to indicate or approximate the real degree of hydration are liberated heat of hydration, amount of chemically bound water, and chemical shrinkage, etc. Thus, the degree of heat liberation and adiabatic temperature rise could be determined by prediction of degree of hydration.

• The Korean Journal of Elementary Counseling
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• v.9 no.2
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• pp.143-162
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• 2010

The purpose of this study is to construct Parent Attachment Scale for Children. Adapting the item consisting method used in Experiences in Close Relationships-Revised(ECR-R), Parent Attachment Scale for Children was constructed to measure child's attachment style with their parent, reliably and validly. Also, reliability and item trait informations based on item response theory were reviewed. First preliminary items were derived from the original items of ECR-R and existing Attachment Inventories. These items were modified and complemented to be easier and keep the original meaning of each item. Second preliminary items were administrated to 4~6th grades students(N=576). Finally, Parent Attachment Scale for Children were consisted with 30 items based on two-parameter graded response model. Internal consistency ranges of the scales of Parent Attachment Scale for Children are as follows : Avoidance scale is .94~.96; Anxiety scale is .85~.88. Test-retest reliability ranges are as follows; Avoidance scale is .71~.80; Anxiety scale is .53~.68. Item discrimination and item information value were within an appropriated range. Hierarchical cluster analysis with Ward's Method revealed four types of attachment style : Secure, Dismissing, Preoccupied, Fearful. Other implications and limitations of this study were discussed.

• Journal of the Korea institute for structural maintenance and inspection
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• v.10 no.3
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• pp.185-196
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• 2006

Fracture toughness is a material property for crack initiation and propagation in fracture mechanics. For mode I fracture toughness measurement in concrete, RILEM committees 89-FMT proposed three-point bend tests based on the two-parameter fracture model. But, there is no proposed test method as a standard for mixed mode test for now. And RILEM three-point bend test procedure is complicate. Therefore, in this study, brazilian disks of various size were designed as the concrete with a similar specified concrete strength and maximum size of coarse aggregate($G_{max}$) were respectively 20mm and 40mm. And mode I fracture toughness of brazilian disks was compared with that of RILEM three-point bend test. As a result, it was suggested appropriate size(thickness, diameter) and notch length ratio of brazilan disk on the $G_{max}$. And it was verified that stress intensity factors for mixed mode can be easily calculated with the disk specimen. Stress intensity factors of a concrete brazilian disk were evaluated with finite element analysis and five terms approximation for comparison.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.15-15
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• 2011

The radar observation system in Japan is operated by two governmental groups: Japan Meteorological Agency (JMA) and the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of Japan. The JMA radar observation network is comprised of 20 C-band radars (with a wavelength of 5.6 cm), which cover most of the Japan Islands and observe rainfall intensity and distribution. And the MLIT's radar observation system is composed of 26 C-band radars throughout Japan. The observed radar echo from each radar unit is first modified, and then sent to the National Bureau of Synthesis Process within the MLIT. Through several steps for homogenizing observation accuracy, including distance and elevation correction, synthesized rainfall intensity maps for the entire nation of Japan are generated every 5 minutes. The MLIT has recently launched a new radar observation network system designed for flash flood observation and forecasting in small river basins within urban areas. It is called the X-band multi parameter radar network, and is distinguished by its dual polarimetric wave pulses of short length (3cm). Attenuation problems resulting from the short wave length of radar echo are strengthened by polarimetric wavelengths and very dense radar networks. Currently, the network is established within four areas. Each area is observed using 3-4 X-band radars with very fine resolution in spatial (250 m) and temporal (1 minute intervals). This study provides a series of utilization procedures for the new input data into a real-time forecasting system. First of all, the accuracy of the X-band radar observation was determined by comparing its results with the rainfall intensities as observed by ground gauge stations. It was also compared with conventional C-band radar observation. The rainfall information from the new radar network was then provided to a distributed hydrologic model to simulate river discharges. The simulated river discharges were evaluated again using the observed river discharge to estimate the applicability of the new observation network in the context of operations regarding flood forecasting. It was able to determine that the newly equipped X-band polarimetric radar network shows somewhat improved observation accuracy compared to conventional C-band radar observation. However, it has a tendency to underestimate the rainfall, and the accuracy is not always superior to that of the C-band radar. The accuracy evaluation of the X-band radar observation in this study was conducted using only limited rainfall events, and more cases should be examined for developing a broader understanding of the general behavior of the X-band radar and for improving observation accuracy.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.5
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• pp.944-958
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• 2011

Soil hydraulic properties such as hydraulic conductivity or water retention which are costly to measure can be indirectly generated by soil pedotransfer function (PTF) using easily obtainable soil data. The field soil structure description which is routinely recorded could also be used in PTF as an input to reduce the uncertainty. The purposes of this study were to use qualitative morphological soil structure descriptions and soil structural index into PTF and to evaluate their contribution in the prediction of soil hydraulic properties. We transformed categorical morphological descriptions of soil structure into quantitative values using categorical principal component analysis (CATPCA). This approach was tested with a large data set from the US National Pedon Characterization database with the aid of a categorical regression tree analysis. Six different PTFs were used to predict the saturated hydraulic conductivity and those results were averaged to quantify the uncertainty. Quantified morphological description was successively used in multiple linear regression approach to predict the averaged ensemble saturated conductivity. The selected stepwise regression model with only the transformed morphological variables and structural index as predictors predicted the $K_{sat}$ with $r^2$ = 0.48 (p = 0.018), indicating the feasibility of CATPCA approach. In a regression tree analysis, soil structure index and soil texture turned out to be important factors in the prediction of the hydraulic properties. Among structural descriptions size class turned out to be an important grouping parameter in the regression tree. Bulk density, clay content, W33 and structural index explained clusters selected by a two step clustering technique, implying the morphologically described soil structural features are closely related to soil physical as well as hydraulic properties. Although this study provided relatively new method which related soil structure description to soil structure index, the same approach should be tested using a datasets containing the actual measurement of hydraulic properties. More insight on the predictive power of soil structure index to estimate hydraulic properties would be achieved by considering measured the saturated hydraulic conductivity and the soil water retention.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.43-50
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• 2021

A Leg Mating Unit (LMU) is a device utilized during the float-over installation of offshore structures that include hyperelastic pads and mating part. The hyperelastic pads absorb the loads, whereas the mating part works as guidance between topside and supporting structures during the mating sequence of float-over installation. In this study, the design optimization of an LMU for the float-over installation of floating-type offshore structures is conducted to enhance the performance and to satisfy the requirements defined by classification society regulations. The initial dimensions of the LMU are referred to the dimensions of those used in fixed-type float-over installation because only the location and the number of LMUs are known. The two-parameter Mooney-Rivlin model is adopted to describe the hyperelastic pads under given material parameters. Geometric variables, such as the thickness, height, and width of members, as well as configuration variables, such as the angle and number of members, are defined as design variables and are parameterized. A sampling-based design sensitivity analysis based on latin hypercube sampling method is performed to filter the important design variables. The design optimization problem is formulated to minimize the total mass of the LMU under maximum von Mises stress and reaction force constraints.

• Clean Technology
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• v.27 no.1
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• pp.61-68
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• 2021

Selective catalytic reduction (SCR) is widely used as a method of removing nitrogen oxide in large-capacity thermal power generation systems. Uniform mixing of the injected ammonia and the inlet flue gas is very important to the performance of the denitrification reduction process in the catalyst bed. In the present study, a computational analysis technique was applied to the ammonia injection system design process of a denitrification facility. The applied model is the denitrification facility of an 800 MW class coal-fired power plant currently in operation. The flow field to be solved ranges from the inlet of the ammonia injection system to the end of the catalyst bed. The flow was analyzed in the two-dimensional domain assuming incompressible. The steady-state turbulent flow was solved with the commercial software named ANSYS-Fluent. The nozzle arrangement gap and injection flow rate in the ammonia injection system were chosen as the design parameters. A total of four (4) cases were simulated and compared. The root mean square of the NH3/NO molar ratio at the inlet of the catalyst layer was chosen as the optimization parameter and the design of the experiment was used as the base of the optimization algorithm. The case where the nozzle pitch and flow rate were adjusted at the same time was the best in terms of flow uniformity.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.