• Journal of Korea Water Resources Association
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• v.49 no.5
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• pp.411-422
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• 2016

Rainfall-runoff modeling in conjunction with rainfall frequency analysis has been widely used for estimating design floods in South Korea. However, uncertainties associated with underlying distribution and sampling error have not been properly addressed. This study applied a Bayesian method to quantify the uncertainties in the rainfall frequency analysis along with Gumbel distribution. For a purpose of comparison, a probability weighted moment (PWM) was employed to estimate confidence interval. The uncertainties associated with design rainfalls were quantitatively assessed using both Bayesian and PWM methods. The results showed that the uncertainty ranges with PWM are larger than those with Bayesian approach. In addition, the Bayesian approach was able to effectively represent asymmetric feature of underlying distribution; whereas the PWM resulted in symmetric confidence interval due to the normal approximation. The use of long period data provided better results leading to the reduction of uncertainty in both methods, and the Bayesian approach showed better performance in terms of the reduction of the uncertainty.

• Journal of the Korea Concrete Institute
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• v.21 no.5
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• pp.573-580
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• 2009

This paper presents the mix composition, production method, and curing condition applied to the extruded ECC(Engineered Cementitious Composite) panel which are able to exhibit multiple cracking and potential pseudo strain-hardening behavior. In addition to the production technique of extruded ECC panel, the effect of fiber distribution characteristics, which are uniquely created by applying extrusion process, on the flexural behavior of the panel is also focussed. In order to demonstrate fiber distribution, a series of experiments and analyses, including image processing/analysis and micro-mechanical analysis, was performed. The optimum mix composition of extruded ECC panel was determined in terms of water matrix ratio, the amount of cement, ECC powder, and silica powder. It was found that flexural behavior of extruded ECC panel was highly affected by the slight difference in mix composition of ECC panel. This is mainly because the difference in mix composition results in the change of micro-mechanical properties as well as fiber distribution characteristics, represented by fiber dispersion and orientation. In terms of the average fiber orientation, the fiber distribution was found to be similar to the assumption of two dimensional random distribution, irrespective of mix composition. In contrast, the probability density function for fiber orientation was measured to be quite different depending on the mix composition.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.48 no.2
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• pp.128-136
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• 2012

Hybrid surveys using hydroacoustic and fish sampling gears such as trammel net, trap were conducted to investigate total biomass, distribution and dominant fish species of the demersal fishes in the marine ranching area (MRA) of Jeju. Four surveys were carried out in June, August, October and November using 38kHz quantitative echo sounder. Catch data using trammel net and trap were used to calculate biomass and to examine dominant fish species. Fish schools were mainly detected in the waters of 20 meters below and around Chagwido waters. By the result of fishing experiments, fish species living in MRA of Jeju were about 40 species, dominant fish species of a detectable fish such as Family Sparidae and Family Monacanthidae etc. were identified 59.4~68.8% of total biomass. Based on the hydroacoustic data, fish length-weight function and target strength information, mean density of the demersal fish estimated were as follows; 0.88g/$m^2$ on June, 1.12g/$m^2$ on August, 1.35g/$m^2$ on October and 1.18g/$m^2$ on November. An estimated average biomass in MRA of Jeju was founded 20.5 tons in 2007, 20.6 tons in 2008, 23.0 tons in 2009, 25.9 tons in 2010. The results showed that biomass of MRA is getting increased slowly. Therefore the hybrid survey method using hydroacoustic and fish sampling gears is assured an effective biomass survey in the waters of mixed species.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.131-140
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• 2009

This paper presents a new algorithm to the segmentation of the FISH images. First, for segmentation of the cell nuclei from background, a threshold is estimated by using the gaussian mixture model and maximizing the likelihood function of gray value of cell images. After nuclei segmentation, overlapped nuclei and isolated nuclei need to be classified for exact nuclei analysis. For nuclei classification, this paper extracted the morphological features of the nuclei such as compactness, smoothness and moments from training data. Three probability density functions are generated from these features and they are applied to the proposed Bayesian networks as evidences. After nuclei classification, segmenting of overlapped nuclei into isolated nuclei is necessary. This paper first performs intensity gradient transform and watershed algorithm to segment overlapped nuclei. Then proposed stepwise merging strategy is applied to merge several fragments in major nucleus. The experimental results using FISH images show that our system can indeed improve segmentation performance compared to previous researches, since we performed nuclei classification before separating overlapped nuclei.

• Korean Journal of Materials Research
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• v.8 no.10
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• pp.884-889
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• 1998

Amorphous silicon films of large area have been crystallized by a line shape excimer laser beam of one dimensional scanning with a gaussian profile in the scanning direction. In order to characterize the crystalline phase transition of thickness variables in excimer laser annealing(ELA), angle wrapping method was used. And also to characterize the residual stresses of crystalline phase transition in the case of angle wrapped-crystalline silicon on corning 7059 glass, polarized raman spectroscopies were measured at various laser energy density and substrate temperature. The residual stress varies from $9.0{\times}10^9$ to $9.9{\times}10^9$, and from $9.9{\times}10^9$ to $1.2{\times}10^10$dyne/${cm}^2$ of the substrate temperature at room temperature and varies from $8.1{\times}10^9$ to $9.0{\times}10^9$, and from $9.0{\times}10^9$ to $9.9{\times}10^9$dyne/${cm}^2$ of the substrate temperature at $400^{\circ}C$ as a function of direction from surface to substrate. According to the direction from the surface in liquid phase to the interface and from the interface to near the substrate in solid phase of recrystallized Si thin film, respectively. Thus, the stress is increased from(Liquid phase to solid phase) with phase transition.

• Korean Chemical Engineering Research
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• v.45 no.3
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• pp.242-248
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• 2007

For an optimal design of automotive electric system, it is important to have a reliable modeling tool to predict the charge-discharge behaviors of the automotive battery. In this work, a two-dimensional modeling was carried out to predict the charge-discharge behaviors of a 12-V automotive lead-acid battery. The model accounted for electrochemical kinetics and ionic mass transfer in a battery cell. In order to validate the modeling, modeling results were compared with the experimental data of the charge-discharge behaviors of a lead-acid battery. The discharge behaviors were measured with three different discharge rates of C/5, C/10, and C/20 at operating temperature of $25^{\circ}C$. The batteries were charged with constant current of 30A until the charging voltage reached to a predetermined value of 14.24 V and then the charging voltage was kept constant. The discharge and charge curves from the measurements and modeling were in good agreement. Based on the modeling, the distributions of the electrical potentials of the solid and solution phases, the porosity of the electrodes, and the current density within the electrodes as well as the acid concentration can be predicted as a function of charge and discharge time.

• Korean Journal of Remote Sensing
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• v.20 no.2
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• pp.91-102
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• 2004

This paper reflects the estimation of using the EOC(Electro-optical Camera) images supporting GIUH(geomorphological instantaneous unit hydrograph) approach. We have analyzed GIUH in its density and frequency distribution by creating a DEM(digital elevation model) for the sub basin produced from the EOC images and examined topographical and hydrological application possibility of the EOC images. In this process, we have topographical basin characteristic analysis that use the remote sensing technique analyzing the DEM creation process of the EOC stereo images by studying the basic topographical hydrology analysis about abstraction technique since it is flirty complex and is more time-consuming than other method. we executed statistical analysis of a basin size and river length using the frequency function after divided lattice spacing applied have to the sub river basin from the image data and the digital map into 10m intervals ranging from 10m to 100m. After comparing and examining the peak and time to peak of the GIUH, we proceeded with a comparative analysis by lattice concerning the topographical divergence rate, area ratio, length ratio. Accumulating the peak and time to peak of the GIUH is altered to non-linear form in accordance to lattice dimension as well as basin factor. It was proved that the lattice dimension is one of the important factors about the peak and time to peak of the GIUH.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.3
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• pp.55-71
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• 2019

The purpose of this study is to propose an evaluation method to assess green environments of streetscapes to improve urban street green spaces in Jeonju City. Through a rapid assessment of urban street green spaces, we suggest an objective basis for expanding street green space as well as for adopting sustainable maintenance and improvement measures. We choose 12 sections of streetscapes (roads and sidewalks) to investigate existing street conditions which have more than four lanes and function as major road axes. Six large roads and six medium roads of Jeonju City center area are investigated as pilot assessment study sites. Site inventory checklists consist of environmental characteristics of streetscape, street tree status, and planting condition evaluation. Environmental characteristics of streetscapes are composed of physical and neighborhood factors. For instance, items for physical factors are types and width of road/sidewalks, paving materials, tree protection materials, and green strip. And surrounding landuse is a neighborhood factor. Assessment items for street tree status are street plant names (tree/shrubs/ground cover), size, and planting intervals. Planting condition evaluation items are tree shape, damage, canopy density, and planting types with existence of adjacent green space. Evaluation results are classified into three levels such as A(maintain or repair), B(greening enhancement), and C(structural improvement). In case of grade A, streetscapes have enough sidewalk width for maintaining green strip and a multi-layered planting(in large road only) with fairly good growing conditions of street trees. For grade B and C, streetscapes have a moderate level of sidewalk width with a single street tree planting. In addition, street tree growing conditions are appeared poor so that green enhancement or maintenance measures are needed. For median, only grade B and C are found as its planting growing foundations are very limited in space. As a result, acquiring enough sidewalk space is essential to enhance ecological quality of urban street green. Especially, it is necessary to have green strip with reasonable widths for plant growing conditions in sidewalks. In addition, we need to consider native species with multi-layer plant compositions while designing street green.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1295-1303
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• 2022

As the role of water distribution networks (WDNs) becomes more important, identifying abnormal events (e.g., pipe burst) rapidly and accurately is required. Since existing approaches such as field equipment-based detection methods have several limitations, model-based methods (e.g., machine learning based detection model) that identify abnormal events using hydraulic simulation models have been developed. However, no previous work has examined the impact of data uncertainties on the results. Thus, this study compares the effects of measurement error-induced pressure data uncertainty in WDNs. An artificial neural network (ANN) is used to predict nodal pressures and measurement errors are generated by using cumulative density function inverse sampling method that follows Gaussian distribution. Total of nine conditions (3 input datasets × 3 output datasets) are considered in the ANN model to investigate the impact of measurement error size on the prediction results. The results have shown that higher data uncertainty decreased ANN model's prediction accuracy. Also, the measurement error of output data had more impact on the model performance than input data that for a same measurement error size on the input and output data, the prediction accuracy was 72.25% and 38.61%, respectively. Thus, to increase ANN models prediction performance, reducing the magnitude of measurement errors of the output pressure node is considered to be more important than input node.