• Journal of Information Processing Systems
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• v.12 no.1
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• pp.149-168
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• 2016

The Active Appearance Model (AAM) is a class of deformable models, which, in the segmentation process, integrates the priori knowledge on the shape and the texture and deformation of the structures studied. This model in its sequential form is computationally intensive and operates on large data sets. This paper presents another framework to implement the standard version of the AAM model. We suggest a distributed and parallel approach justified by the characteristics of the model and their potentialities. We introduce a schema for the representation of the overall model and we study of operations that can be parallelized. This approach is intended to exploit the benefits build in the area of advanced image processing.

• Journal of Digital Contents Society
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• v.18 no.4
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• pp.769-775
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• 2017

The International Maritime Organization's e-Navigation strategy is to introduce new technologies to ships to support easier and safer navigation. With the e-Navigation strategy, various equipment will be installed in vessels and the system structure will be linked to onshore systems. For this reason, a common data structure between systems became necessary, and finally the S-100 standard developed by the International Hydrographic Organization was selected. This paper describes a design of marine casualty data model based on the S-100 standard. The data model of the S-100 standard is designed in the form of a UML class diagram, and the final encoding follows the GML / XML format. We will look at the S-100 standard and product specifications under development, and describe the S-100 standards-based data design and portrayal definition of marine accident data.

• Nuclear Engineering and Technology
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• v.36 no.3
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• pp.248-262
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• 2004

Turbulence models are separately assessed for a three dimensional thermal-hydraulic analysis of the integral reactor SMART. Seven models (mixing length, k-l, standard $k-{\epsilon},\;k-{\epsilon}-f{\mu},\;k-{\epsilon}-v2$, RRSM, and ERRSM) are investigated for flat plate channel flow, rotating channel flow, and square sectioned U-bend duct flow. The results of these models are compared to the DNS data and experiment data. The results are assessed in terms of many aspects such as economical efficiency, accuracy, theorization, and applicability. The standard $k-{\epsilon}$ model (high Reynolds model), the $k-{\epsilon}-v2$ model, and the ERRSM (low Reynolds models) are selected from the assessment results. The standard $k-{\epsilon}$ model using small grid numbers predicts the channel flow with higher accuracy in comparison with the other eddy viscosity models in the logarithmic layer. The elliptic-relaxation type models, $k-{\epsilon}-v2$, and ERRSM have the advantage of application to complex geometries and show good prediction for near wall flows.

• Journal of Information Technology Applications and Management
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• v.17 no.3
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• pp.163-189
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• 2010

The purpose of this research is to develop a decision-making model for the calculation of the National Disaster Management System's standard prevention workforce quota. The final purpose of such model is to support in arranging a rationally sized prevention workforce for local authorities by providing information about its calculation in order to support an effective and efficient disaster management administration. In other words, it is to establish and develop a model that calculates the standard disaster prevention workforce quota for basic local governments in order to arrange realistically required prevention workforce. In calculating Korea's prevention workforce, it was found that the prevention investment expenses, number of prevention facilities, frequency of flood damage, number of disaster victims, prevention density, and national disaster recovery costs have positive influence on the dependent variable when the standard prevention workforce was set as the dependent variable. The model based on the regression analysis-which consists of dependent and independent variables-was classified into inland mountainous region, East coast region, Southwest coastal plain region to reflect regional characteristics for the calculation of the prevention workforce. We anticipate that the decision-making model for the standard prevention workforce quota will aid in arranging an objective and essential prevention workforce for Korea's basic local authorities.

• Wind and Structures
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• v.36 no.4
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• pp.237-247
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• 2023

The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.138-143
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• 2006

In this paper, we have presented a Sequential Agglomerative Hierarchical Nested (SAHN) algorithm-based data clustering method in fuzzy inference system to achieve optimal performance of fuzzy model. SAHN-based algorithm is used to give possible range of number of clusters with cluster centers for the system identification. The axes of membership functions of this fuzzy model are optimized by using cluster centers obtained from clustering method and the consequence parameters of the fuzzy model are identified by standard least square method. Finally, in this paper, we have observed our model's output performance using the Box and Jenkins's gas furnace data and Sugeno's non-linear process data.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.21 no.9
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• pp.1149-1164
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• 1997

In this study, the applicability of the RNG k-.epsilon. model to the analysis of the complex flows is studied. The governing equations based on a non-orthogonal coordinate formulation with Cartesian velocity components are used and discretized by the finite volume method with non-staggered variable arrangements. The predicted results using the RNG k-.epsilon. model of three complex flows, i.e., the flow over a backward-facing step and a blunt flat plate, the flow around a 2D model car are compared to these from the standard k-.epsilon. model and experimental data. That of the unsteady axisymmetric turbulent flow within a cylinder of reciprocating model engine including port/valve assembly and the spray characteristics within a chamber of direct injection model engine are compared to these from the standard k-.epsilon. model and experimental data. The results of reattachment length, separated eddy size, average surface pressure distribution using the RNG k-.epsilon. model show more reasonable trends comparing with the experimental data than those using the modified k-.epsilon. model. Although the predicted rms velocity using the modified k-.epsilon. model is lower considerably than the experimental data in incylinder flow with poppet valve, predicted axial and radial velocity distributions at the valve exit and in-cylinder region show good agreements with the experimental data. The spray tip penetration predicted using the RNG k-.epsilon. model is more close to the experimental data than that using the modified k-.epsilon. model. The application of the RNG k-.epsilon. model seems to have some potential for the simulations of the unsteady turbulent flow within a port/valve-cylinder assembly and the spray characteristics over the modified k-.epsilon. model.

• Fashion & Textile Research Journal
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• v.11 no.5
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• pp.779-787
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• 2009

The purpose of this study was to classify the head shape for the apparel industry and to suggest standard head model for korean women. The 23 measurement items of 891 females, aged more than 18 years were used to analysis by statistical methods. Factor analysis, cluster analysis and duncan test were performed using these data. Through factor analysis, 5 factors were extracted upon factor scores and those factors comprised 68.76% for the total variances. 5 clusters as their head and face shape were categorized. We decided for the type 3 to standard head shape. 24 participants were measured using computed tomography(CT). The measured data of skin and skeleton and the standard head shapes were illustrated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.334-337
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• 2021

Recently, the necessity of rapid response to social problems such as disasters and quarantine arising from the complex and diverse social structures has emerged. As the number of large buildings increases, large-scale human damage is expected in the event of a disaster such as fire. To solve this problem, efficient control must be achieved through interfacing with various equipment and facilities installed inside the building. In this paper, we intend to study the interface method for various facilities in the building for efficient control. In detail, the facility standard model is defined by examining the status and specification of building. In addition, we intend to design and propose a standard facility communication data frame to support the protocol applicable to this model.

• Journal of Mechanical Science and Technology
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• v.14 no.1
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• pp.93-102
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• 2000

The objective of the present study is to investigate the pressure-strain correlation terms of the Reynolds stress models for the three dimensional turbulent boundary layer in a $30^{\circ}$ bend tunnel. The numerical results obtained by models of Launder, Reece and Rodi (LRR) , Fu and Speziale, Sarkar and Gatski (SSG) for the pressure-strain correlation terms are compared against experimental data and the calculated results from the standard k-${\varepsilon}$ model. The governing equations are discretized by the finite volume method and SIMPLE algorithm is used to calculate the pressure field. The results show that the models of LRR and SSG predict the anisotropy of turbulent structure better than the standard k-${\varepsilon}$ model. Also, the results obtained from the LRR and SSG models are in better agreement with the experimental data than those of the Fu and standard k-${\varepsilon}$ models with regard to turbulent normal stresses. Nevertheless, LRR and SSG models do not effectively predict pressure-strain redistribution terms in the inner layer because the pressure-strain terms are based on the locally homogeneous approximation. Therefore, to give better predictions of the pressure-strain terms, non-local effects should be considered.