• Journal of the Korean Mathematical Society
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• v.61 no.5
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• pp.875-898
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• 2024

This study estimates the true price of an asset and finds the optimal bid/ask prices for market makers. We provide a novel state-space model based on the exponential Ornstein-Uhlenbeck volatility and the Heston models with Gaussian noise, where the traded price and volume are available, but the true price is not observable. An objective of this study is to use Bayesian filtering to estimate the posterior distribution of the true price, given the traded price and volume. Because the posterior density is intractable, we employ the guided particle filtering algorithm, with which adaptive rejection metropolis sampling is used to generate samples from the density function of an unknown distribution. Given a simulated sample path, the posterior expectation of the true price outperforms the traded price in estimating the true price in terms of both the mean absolute error and root-mean-square error metrics. Another objective is to determine the optimal bid/ask prices for a market maker. The profit-and-loss of the market maker is the difference between the true price and its bid/ask prices multiplied by the traded volume or bid/ask size of the market maker. The market maker maximizes the expected utility of the PnL under the posterior distribution. We numerically calculate the optimal bid/ask prices using the Monte Carlo method, finding that its spread widens as the market maker becomes more risk-averse, and the bid/ask size and the level of uncertainty increase.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.127-135
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• 2006

The image-based 3D modeling is the technique of generating a 3D graphic model from images acquired using cameras. It is being researched as an alternative technique for the expensive 3D scanner. In this paper, I propose the image-based 3D modeling system using calibrated camera. The proposed algorithm for rendering 3D model is consisted of three steps, camera calibration, 3D shape reconstruction and 3D surface generation step. In the camera calibration step, I estimate the camera matrix for the image aquisition camera. In the 3D shape reconstruction step, I calculate 3D volume data from silhouette using pyramidal volume intersection. In the 3D surface generation step, the reconstructed volume data is converted to 3D mesh surface. As shown the result, I generated relatively accurate 3D model.

• Journal of Soil and Groundwater Environment
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• v.17 no.6
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• pp.69-81
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• 2012

Diverse geostatistical tools such as kriging have been used to estimate the volume and spatial coverage of contaminated soil needed for remediation. However, many approaches frequently yield estimation errors, due to inherent geostatistical uncertainties. Such errors may yield over- or under-estimation of the amounts of polluted soils, which cause an over-estimation of remediation cost as well as an incomplete clean-up of a contaminated land. Therefore, it is very important to use a better estimation tool considering uncertainties arising from incomplete field investigation (i.e., contamination survey) and mathematical spatial estimation. In the current work, as better estimation tools we propose stochastic simulation approaches which allow the remediation volume to be assessed more accurately along with uncertainty estimation. To test the efficiency of proposed methods, heavy metals (esp., Pb) contaminated soil of a shooting range area was selected. In addition, we suggest a quantitative method to delineate the confident interval of estimated volume (and spatial extent) of polluted soil based on the spatial aspect of uncertainty. The methods proposed in this work can improve a better decision making on soil remediation.

• Korean Journal of Environmental Biology
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• v.29 no.4
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• pp.312-320
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• 2011

In order to estimate a better methodological factor to understand phytoplankton ecology between abundance and bio-volume of phytoplankton, each 1,160 phytoplankton data, including abundance, classification and chlorophyll $a$ concentration were collected in Korean coastal waters of Incheon (Yellow sea), Tongyeong (South sea), and Ulsan (East sea). Based on these data, phytoplankton bio-volume can be calculated through a geometric model. The correlation coefficient between abundance and chlorophyll $a$ concentration was higher than the coefficient between biovolume and chlorophyll $a$ concentration, because a small size phytoplankton has relatively dense chlorophyll contents compared with the proportion of chlorophyll in a large size phytoplankton. Thus, the interpretation using abundance to understand phytoplankton ecology in Korean coastal waters may be more effective than that using bio-volume.

• The KIPS Transactions:PartC
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• v.18C no.1
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• pp.31-44
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• 2011

The applicability of network-based computing depends on the availability of the underlying network bandwidth. Such a growing gap between the capacity of the backbone network and the end users' needs results in a serious bottleneck of the access network in between. As a result, ISP incurs disadvantages in their business. If this situation is known to ISP in advance, or if ISP is able to predict traffic volume end-to-end link high-load zone, ISP and end users would be able to decrease the gap for ISP service quality. In this paper, simulation tools, such as ACE, ADM, and Flow Analysis, were used to be able to perceive traffic volume prediction and end-to-end link high-load zone. In using these simulation tools, we were able to estimate sequential transaction in real-network for e-Commerce. We also imported virtual network environment estimated network data, and create background traffic. In a virtual network environment like this, we were able to find out simulation results for traffic volume prediction and end-to-end link high-load zone according to the increase in the number of users based on virtual network environment.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.3
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• pp.210-216
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• 2009

Several empty-space leaping methods have been proposed for CPU-based volume ray casting. When sample points are located in semi-transparent cells, however, previous leaping methods perform unnecessary resamplings even if the scalar values on those points are confined within transparent range. A semi-transparent cells leaping method for volume ray casting using the Marching Cubes algorithm is proposed to solve this problem in our previous work. When a ray reaches a semi-transparent cell, our method performs in-out test between current sample point and the bounding box enclosing the triangles generated by the Marching Cubes. If the sample point lies on outside of the bounding box, we estimate the point is regarded as transparent. In this case, the ray advances to the next sample point without performing a resampling operation. We can frequently refer the tables for neighboring voxels, however, when we exploit conventional data structures of the Marching Cubes. We propose modified Marching Cubes tables for solving this problem.

• Journal of the Korea Organic Resources Recycling Association
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• v.15 no.4
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• pp.138-146
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• 2007

There are still lots of areas where are covered with combined sewer pipes in Seoul. All buildings within those areas are equipping septic tanks which take part in separating solids from flushing water of chamber pots. Septic tanks legally demand emptying and cleaning the those inner bodies, resulting the generation of sludge which should be purified using the specified treatment plants, as one of environmental infrastructures. Scale of treatment plants for septic tank sludge are affected by sludge volume generated from cleaning, which give emphasis to adequate estimation of sludge volume in the future. This study aimed to define prediction tools for sludge volume. Among various parameters, floor area of building is most reasonable one to estimate the quantity of cleaning sludge, showing increasing gradually up to 13,149kL a day in 2020. Using same parameter also are able to assess the amount of BOD in the cleaning sludge.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.753-756
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• 2008

HPFRCC (High-Performance Fiber Reinforced Cementitious Composites), which is relatively more ductile and has the characteristic of high toughness with high fiber volume fractions, can be used in structures subjected to extreme loads and exposed to durability problems. In the case of using PVA(polyvinyl alcohol) fibers, it is noted by former studies that around 2% fiber volume fractions contributes to the most effective performance at HPFRCC. In this study, therefore, compressive and flexural tests were implemented to evaluate the compressive and flexural capacities of HPFRCC while the total fiber volume fractions was fixed at 2% and two different PVA fibers were used with variable fiber volume fractions to control the micro-crack and macro-crack with short and long fibers, respectively. Moreover, specimens reinforced with steel and PVA fiber simultaneously were also tested to estimate their behavior and finally find out the optimized mixture. In the result of these experiments, the specimen consists of 1.6% short fibers (REC 15) and 0.4% long fiber (RF4000) outperformed other specimens. When a little steel fibers added to the mixture with 2% PVA fibers, the flexural capacity was increased, however, when high steel fiber volume fractions applied, the flexural capacity was decreased.

• Smart Media Journal
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• v.9 no.2
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• pp.22-32
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• 2020

The hippocampal volume atrophy is known to be linked with neuro-degenerative disorders and it is also one of the most important early biomarkers for Alzheimer's disease detection. The measurements of hippocampal pure volumes from Magnetic Resonance Imaging (MRI) is a crucial task and state-of-the-art methods require a large amount of time. In addition, the structural brain development is investigated using MRI data, where brain morphometry (e.g. cortical thickness, volume, surface area etc.) study is one of the significant parts of the analysis. In this study, we have proposed a patch-based ensemble model of 3-D convolutional neural network (CNN) to measure the hippocampal pure volume from MRI data. The 3-D patches were extracted from the volumetric MRI scans to train the proposed 3-D CNN models. The trained models are used to construct the ensemble 3-D CNN model and the aggregated model predicts the pure volume in one-step in the test phase. Our approach takes only 5 seconds to estimate the volumes from an MRI scan. The average errors for the proposed ensemble 3-D CNN model are 11.7±8.8 (error%±STD) and 12.5±12.8 (error%±STD) for the left and right hippocampi of 65 test MRI scans, respectively. The quantitative study on the predicted volumes over the ground truth volumes shows that the proposed approach can be used as a proxy.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.4
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• pp.99-106
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• 2009

This research was to estimate quantitative infiltration volume of permeable block which is one of runoff reduction infiltration facilities. In this research, the permeable block experiments estimating infiltration volume for 50, 100, 150, 200 mm/hr rainfall intensity were carried out and hydraulic experiments results were compared with numerical simulation output to produce feasibility of numerical simulation. Final infiltration capacity analysis of permeable block hydraulic experiments reveals that every estimated infiltration volume before runoff beginning was above approximately 300.0 l despite rapid reduction of infiltration ratio and runoff initiation time were occurred in every rainfall intensity. Statistical calculation for coefficient of determination based on cumulative infiltration volume of hydraulic experiment and numerical simulation resulted in a high correlationship as $0.958{\sim}0.996$.