• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2232-2245
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• 2013

The present study proposes efficient procedural modeling methods for enabling the growth and creation of various trees with minimal user control. Growth volume algorithms are utilized in order to easily and effectively calculate many parameters that determine tree growth, including branch propagation. Procedural methods are designed so that users' interactive control structures can be applied to these algorithms to create unique tree models efficiently. First, through a two-line-based interactive growth volume control method, the growth information that determines the overall shape of the tree is intuitively adjusted. Thereafter, independent branch control methods designed to control individual branches are added to the growth deformation in order to enable the growth of unique trees. Whether the growth processes of desired trees can be easily and intuitively controlled by the proposed method is verified through experiments. Methods that can apply the proposed methods are also verified.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.346-351
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• 2023

With the rapid development of Artificial Intelligence (AI) technology, there is an increasing variety of methods for creating 3D models. These include innovations such as text-only generation, 2D images to 3D models, and combining images with cue words. Each of these methods has unique advantages, opening up new possibilities in the field of 3D modeling. The purpose of this study is to explore and summarize these methods in-depth, providing researchers and practitioners with a comprehensive perspective to understand the potential value of these methods in practical applications. Through a comprehensive analysis of pure text generation, 2D images to 3D models, and images with cue words, we will reveal the advantages and disadvantages of the various methods, as well as their applicability in different scenarios. Ultimately, this study aims to provide a useful reference for the future direction of AI modeling and to promote the innovation and progress of 3D model generation technology.

• Korean Journal of Radiology
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• v.22 no.10
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• pp.1697-1707
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• 2021

The recent introduction of various high-dimensional modeling methods, such as radiomics and deep learning, has created a much greater diversity in modeling approaches for survival prediction (or, more generally, time-to-event prediction). The newness of the recent modeling approaches and unfamiliarity with the model outputs may confuse some researchers and practitioners about the evaluation of the performance of such models. Methodological literacy to critically appraise the performance evaluation of the models and, ideally, the ability to conduct such an evaluation would be needed for those who want to develop models or apply them in practice. This article intends to provide intuitive, conceptual, and practical explanations of the statistical methods for evaluating the performance of survival prediction models with minimal usage of mathematical descriptions. It covers from conventional to deep learning methods, and emphasis has been placed on recent modeling approaches. This review article includes straightforward explanations of C indices (Harrell's C index, etc.), time-dependent receiver operating characteristic curve analysis, calibration plot, other methods for evaluating the calibration performance, and Brier score.

• Archives of design research
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• v.13
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• pp.83-91
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• 1996

In this paper, proposed through the development process of a VCR the process of making exact three-dimensional modeling data using the Alias-one of the popular workstation in product design development process. At first, the features of the NURBS curve which is the basic curve in the modeling of Alias system and the methods of building surface are reviewed. The methods of rendering, lighting and textures are reviewed for application of realistic image presentation after building basic surfaces. The synthetic application methode of these factors necessary for the product design modeling is presented through the development process of a VCR.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.3
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• pp.175-190
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• 1997

Organizational change projects such as Business Process Redisign (BPR) have been perceived to incur high risk due to their high management complexity, enterprise-wide impace, and steep project cost. This research intends to reduce such risk by developing a systematic process redesign methods, called Dynamic Process Modeling (DPM) method. DPM integrates the customer-oriented business process modeling technique with computerized visual simulation technique to promote better understanding of the target process and enable performance simulation of the proposed redesign alternatives prior to actual BPR implementations. For the cusstomer-oriented process modeling, we propose Dynamic-Event Process Chain (Dynamic-EPC) extending from the conceptual customer process model, Event-Process Chain (EPC). We compare DPM with four other implementation-level process modeling methods over eight criteria and demonstrate its effectiveness by applying it to the real-world hospital BPR case.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.451-459
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• 2008

Accurate load modeling is essential for power system static and dynamic analysis. By the nature of the problem of parameter estimation for power system load modeling using actual measurements, multiple local optimal solutions may exist and local methods can be trapped in a local optimal solution giving possibly poor performance. In this paper, Trust-Tech, a novel methodology for global optimization, is applied to tackle the multiple local optimal solutions issue in measurement-based power system load modeling. Multiple sets of parameter values of a composite load model are obtained using Trust-Tech in a deterministic manner. Numerical studies indicate that Trust-Tech along with conventional local methods can be successfully applied to power system load model parameter estimation in measurement-based approaches.

• Knowledge Management Research
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• v.20 no.2
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• pp.1-24
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• 2019

This tutorial introduces procedures and methods for performing structural equation modeling using R. To do this, we present advanced analysis methods based on structural equation model such as mediation effect analysis, moderation effect analysis, moderated mediation effect analysis, and multiple-group analysis with R program code using R lavaan package that supports structural equation modeling. R is flexible and scalable, unlike traditional commercial statistical packages. Therefore, new analytical techniques are likely to be implemented ahead of any other statistical package. From this point of view, R will be a very appropriate choice for applying new analytical techniques or advanced techniques that researchers need. Considering that various studies in the social sciences are applying structural equations modeling techniques and increasing interest in open source R, this tutorial is expected to be useful for researchers who are looking for alternatives to existing commercial statistical packages.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.25-30
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• 1997

Genetic Algorithm(GA) is a parallel, global search technique modeled with the Darwinian principle of survival and reproduction of the fittest. Since Holland has proposed GA called the Simple GA, considerable research has focused of improving Simple GA performance. In this paper, I describe some methods of GA's modeling in different field.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.108-112
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• 1986

Several algorithms for bivariate time series modeling are reviewed : linear least square, nonlinear least squares, generalized least square, and multi-stage least square methods. Estimation results of simulated data by the above methods are discussed.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.6
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• pp.773-785
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• 2014

For decades, modeling of pilots has been intensively studied due to its advantages in reducing costs for training and enhancing safety of pilots. In particular, research for modeling of pilots' non-kinetic behaviors which refer to the decisions made by pilots is beneficial as the expertise of pilots can be inherent in the models. With the recent growth in the amount of combat logs accumulated, employing statistical learning methods for the modeling becomes possible. However, the combat logs consist of heterogeneous data that are not only continuous or discrete but also sequence independent or dependent, making it difficult to directly applying the learning methods without modifications. Therefore, in this paper, we present a kernel function named hybrid sequence kernel which addresses the problem by using multiple kernel learning methods. Based on the empirical experiments by using combat logs obtained from a simulator, the proposed kernel showed satisfactory results.