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

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.37 no.10
• /
• pp.1305-1313
• /
• 2013

In this study, a method to apply feature-based simplification to boundary representation models is proposed. For feature-based simplification, a volume decomposition tree is created from a boundary representation model. The volume decomposition tree is represented by regularized Boolean operations of additive volumes, subtractive volumes, and fillet/round/chamfer volumes, and it is generated by stepwise volume decomposition, which consists of fillet/round/chamfer decomposition, wrap-around decomposition, volume split decomposition, and cell-based decomposition. After the volume decomposition tree is transformed to an infix expression, the CAD model can be simplified by reordering the volumes. To verify the proposed method, a prototype system was implemented, and experiments on test cases were conducted. From the results of the experiments, it is verified that the proposed method is useful for simplifying CAD models based on boundary representation.

• Journal of Navigation and Port Research
• /
• v.44 no.3
• /
• pp.187-194
• /
• 2020

It is very important to forecast freight volume accurately to establish major port policies and future operation plans. Thus, related studies are being conducted because of this importance. In this paper, stepwise regression analysis and artificial neural network model were analyzed to compare the predictive power of each model on Gwangyang Port, the largest domestic port for coal and iron ore transportation. Data of a total of 121 months J anuary 2009-J anuary 2019 were used. Factors affecting coal and iron ore trade volume were selected and classified into supply-related factors and market/economy-related factors. In the stepwise regression analysis, the tonnage of ships entering the port, coal price, and dollar exchange rate were selected as the final variables in case of the Gwangyang Port coal volume forecasting model. In the iron ore volume forecasting model, the tonnage of ships entering the port and the price of iron ore were selected as the final variables. In the analysis using the artificial neural network model, trial-and-error method that various Hyper-parameters affecting the performance of the model were selected to identify the most optimal model used. The analysis results showed that the artificial neural network model had better predictive performance than the stepwise regression analysis. The model which showed the most excellent performance was the Gwangyang Port Coal Volume Forecasting Artificial Neural Network Model. In comparing forecasted values by various predictive models and actually measured values, the artificial neural network model showed closer values to the actual highest point and the lowest point than the stepwise regression analysis.

• The korean journal of orthodontics
• /
• v.23 no.2 s.41
• /
• pp.275-282
• /
• 1993

When mandibular prognathic patients are operated orthognathic surgery which reduce the oral cavity volume, tongue volume should be harmonized with oral cavity volume to be changed because prevent the relapse of malocclusion. To decide the need for tongue resection, the author measured the tongue volume and oral cavity volume with stone model which were taken by impression method and study the difference between the ratio of tongue and oral cavity volume in mandibular prognathic group and normal group. The samples were consisted of four groups, the 40 subjects of the control group, 40 subjects of the experimental group. Each group was subdivided into male and female group respectively. The results were as follows : 1. The tongue volume and oral cavity volume measurements are $20.7cm^3,\;32.7cm^3$ in the control group respectively, and $24.9cm^3,\;42.9cm^3$ in the experimental group respectively. 2. There is no difference in the ratio of the tongue volume to oral cavity volume in control group and experimental group. 3. Correlation coefficients between the tongue volume and oral cavity volume are 0.11, 0.29 in experimental group and control group respectively, and 0.43 in gross total group. 4. The tongue volume of male is larger than female(p<0.05).

• Korean Journal of Computational Design and Engineering
• /
• v.19 no.1
• /
• pp.50-60
• /
• 2014

In this paper, we propose a non-overlapping volume decomposition method that decomposes a solid model into non-overlapped volumes. The non-overlapping volume decomposition finds non-overlapped volumes from maximum volumes. After introducing the concept of the non-overlapping volume decomposition, we discuss technical issues and solutions for them. The non-overlapping volume decomposition is verified by the experiments with a prototype system. From the experiments, it was found that the non-overlapping volume decomposition shows better result than maximum volume decomposition from the view point of design feature recognition.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.05b
• /
• pp.1174-1178
• /
• 2005

In this study, flood inundations have been simulated by using the numerical model FLUMEN solving the shallow-water equations with a finite volume method. Before applying to a real problem, the numerical model is first applied to simplified problems. Obtained numerical results are verified by comparing to available analytical solutions and laboratory measurements. Reasonable agreements are observed. The model is then applied to a simulation of flood events with real geometries. The results of the present study provide basic informations for a flood inundation map.

• Journal of Pharmaceutical Investigation
• /
• v.10 no.1
• /
• pp.1-3
• /
• 1980

The model of an isolated organ system has been developed to simulate the kinetic behavior of drug levels in an acting organ or site. The model is developed from basic considerations of drug distribution with hemodynamical and pharmacokinetical meanings. Model: It is considered a situation in which non-metabolic drug substance is injected into the arterial inflow of an isolated organ at constant rate. The volume of distribution and the concentration of drug in the venous outflow can be mathematically expressed as a function of time.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.38 no.10
• /
• pp.1430-1437
• /
• 2009

Functional bread was manufactured with single cell detritus (SCD) of sea tangle. The optimum ingredient formula for SCD bread was determined based on mixture model. Flour and water reduced max weight, strength, hardness and specific loaf volume, whereas the increased SCD reversed the volume change of dough. Flour increased $L^*$ and $b^*$ values of SCD bread, while SCD decreased. Flour and water decreased $a^*$ value, while SCD increased. Max weight, strength, hardness, specific loaf volume, $b^*$ value and water holding capacity (WHC) were linear model on ANOVA table, whereas distance, volume change of dough, $L^*$ and $a^*$ values were nonlinear model. The response constraint coefficient showed that SCD influenced texture of SCD bread more than flour and water did, whereas water influenced the volume change of dough, specific loaf volume and WHC more than flour and SCD did. Moreover, flour influenced color value more than did water and SCD. Distance and $a^*$ value fitted nonlinear model with interaction terms for flour-SCD and water-SCD. Optimum ingredient formula for SCD bread was: flour, 48.25%; water, 30.89%; SCD, 3.86%. Sensory evaluation of SCD bread was a little lower than industrial bread and electrolyzed SCD bread.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2004.05a
• /
• pp.109-117
• /
• 2004

In this paper, we consider the aortic sinus baroreceptor, which is the most representative baroreceptor sensing the variance of pressure in the cardiovascular system, and propose heart activity control model to observe the effect of delay time in heart period and stroke volume under the regulation of baroreflex in the aortic sinus. The proposed heart activity baroreflex regulation model contains electric circuit sub-model. We constituted the time delay sub-model to observe sensitivity of heart activity baroreflex regulation model by using the variable value to represent the control signal transmission time from the output of baroreflex regulation model to efferent nerve through central nervous system. The simulation object of this model is to observe variability of the cardiovascular system by variable value in time delay sub-model. As simulation results, we observe three patterns of the cardiovascular system variability by the time delay, First, if the time delay over 2.5 second, aortic pressure and stroke volume and heart rate is observed nonperiodically and observed. Finally, if time delay under 0.1 second, then heart rate and aortic pressure-heart rate trajectory is maintained in stable state.

• Journal of Mechanical Science and Technology
• /
• v.20 no.11
• /
• pp.1891-1897
• /
• 2006

In this study, a new model to predict the effective elastic constants of composites with spherical fillers is proposed. The original Eshelby model is extended to a finite filler volume fraction without using Mori-Tanaka's mean field approach. When single filler is embedded in the matrix, the effective elastic constants of the composite are computed. The composite is in turn considered as a new matrix, where new single filler is again embedded in the matrix. The predicted results by the present model with a series of embedding procedures are compared with those by Mori-Tanaka, self-consistent, and generalized self-consistent models. It is revealed through parametric studies such as stiffness ratio of the filler to the matrix and filler volume fraction that the present model gives more accurate predictions than Mori-Tanaka model without using the complicated numerical scheme used in self-consistent and generalized self-consistent models.