• Korean Journal of Computational Design and Engineering
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• v.1 no.1
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• pp.20-32
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• 1996

The boundary evaluation technique for Boolean operation on non-manifold models which is regarded as the most popular and powerful method to create and modify 3-D CAD models has been developed. This technique adopted the concept of Merge and Selection in which the CSG tree for Boolean operation can be edited quickly and easily. In this method, the merged set which contains complete information about primitive models involved is created by merging primitives one by one, then the alive entities are selected following the given CSG tree. This technique can support the hybrid representation of B-rep(Boundary Representation) and CSG(Constructive Solid Geometry) tree in a unified non-manifold model data structure, and expected to be used as a basic method for many modeling problems such as data representation of form features, and the interference between them, and data representation of conceptual models in design process, etc.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.4
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• pp.191-196
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• 2015

In this paper, we present a path-smoothing algorithm for a robot arm manipulator that finds the path using a joint space-based rapidly-exploring random tree. Unlike other smoothing algorithms which require complex mathematical computation, the proposed path-smoothing algorithm is done using a Gaussian process. To find the optimal hyperparameters of the Gaussian process, we use differential evolution hybridized with opposition-based learning. The simulation result indicates that the Gaussian process whose hyperparameters were optimized by hybrid differential evolution successfully smoothed the path generated by the joint space-based rapidly-exploring random tree.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.21 no.4
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• pp.27-33
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• 2013

In this study, the FMEA and FTA for reliability analysis of hybrid rocket motor are performed, that was designed in the Hybrid Rocket Propulsion Laboratory of Korea Aerospace University. In order to carry out these analyses the structure of the hybrid rocket motor is hierarchically divided into 36 parts down to the component level and FMEA is carried out with 72 failure modes. Reliability is assessed based on the FMEA, and the results are used in the FTA to evaluate the overall system reliability. In the FMEA, the relationship between the cause and failure modes, effects and their risk priorities are evaluated qualitatively. 27 failure modes are chosen as those with the critical severity that should be improved with priority. As a result of the FMEA / FTA study, a series of design or material changes are made for the improvement of reliability.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.269-278
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• 2006

This paper presents the new hybrid data mining technique using error pattern, modeling of improving classification accuracy. The proposed method improves classification accuracy by combining two different supervised learning methods. The main algorithm generates error pattern modeling between the two supervised learning methods(ex: Neural Networks, Decision Tree, Logistic Regression and so on.) The Proposed modeling method has been applied to the simulation of 10,000 data sets generated by Normal and exponential random distribution. The simulation results show that the performance of proposed method is superior to the existing methods like Logistic regression and Discriminant analysis.

• Journal of Information Technology Applications and Management
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• v.15 no.1
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• pp.225-242
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• 2008

PM (Inconsistency Pattern Modeling) is a hybrid supervised learning technique using the inconsistence pattern of input variables in mining data sets. The IPM tries to improve prediction accuracy by combining more than two different supervised learning methods. The previous related studies have shown that the IPM was superior to the single usage of an existing supervised learning methods such as neural networks, decision tree induction, logistic regression and so on, and it was also superior to the existing combined model methods such as Bagging, Boosting, and Stacking. The objectives of this paper is explore the characteristics of the IPM. To understand characteristics of the IPM, three experiments were performed. In these experiments, there are high performance improvements when the prediction inconsistency ratio between two different supervised learning techniques is high and the distance among supervised learning methods on MDS (Multi-Dimensional Scaling) map is long.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.578-580
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• 1998

The paper presents an approach to the structure identification based on the CART (Classification And Regression Tree) algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy system. By using the CART algorithm, the proposed method can roughly estimate the numbers of membership function and fuzzy rule using the centers of decision regions. Then the parameter identification is carried out by the hybrid learning scheme using BP (Back-propagation) and RLSE (Recursive Least Square Estimation) from the numerical data. Finally, we will show it's usefulness for fuzzy modeling to truck backer upper control.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3B
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• pp.305-313
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• 2004

Group multicast refers to the kind of multicast in which every member of a group is allowed to transmit data to the group. The goal of routing algorithms for group multicast is to construct a set of low cost multicast trees including all the group members with QoS (e.g., bandwidth) constraint. There have been several algorithms proposed: source tree and shared tree approaches. However, the latter approach has a low success rate in constructing a shared multicast tree, and the former approach suffers from high control overhead and low scalability as stoup size increases. In this paper, we present a heuristic algorithm which varies the number of multicast trees according to the network load. The simulation results show not only that our algorithm outperforms the shared tree approach in terms of the success rate, but also that it has lower control overhead than the source tree approach while guaranteeing the same success rate.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.81-88
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• 2009

In the previous work, we presented HDTS for locating logical errors in Java programs. The HDTS locates an erroneous method at an execution tree using an algorithmic program debugging technique and locates a statement with errors in the erroneous method using a step-wise program debugging. The technique can remove the unnecessary statements and nodes in debugging using a program slicing technique at the execution tree. So HDTS reduces the number of program debugging. In this paper, we design HDTS system for debugging java programs. We define small subset of Java language and design the translator that translates java source codes and the virtual machine that runs java programs. We design GUI(Graphical User Interface) for debugging.

• Korean Journal of Computational Design and Engineering
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• v.7 no.4
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• pp.240-247
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• 2002

Most commercial CAD systems provide parametric modeling functions, and by using these capabilities designers can edit a CAD model in order to create design variants. It is necessary to transfer parametric information during a CAD model exchange to modify the model inside the receiving system. However, it is not possible to exchange parametric information of CAD models based on the cur-rent version of STEP. The designer intents which are contained in the parametric information can be lost during the STEP transfer of CAD models. This paper introduces a hybrid CAB model translator, which also uses the feature tree of commercial CAD systems in addition to the macro file to allow transfer of parametric information. The macro-parametric approach is to exchange CAD models by using the macro file, which contains the history of user commands. To exchange CAD models using the macro-parametric approach, the modeling commands of several commercial CAD systems are analyzed. Those commands are classified and a set of standard modeling commands has been defined. As a neutral fie format, a set of standard modeling commands has been defined. Mapping relations between the standard modeling commands set and the native modeling commands set of commercial CAD systems are defined. The scope of the current version is limited to parts modeling and assemblies are excluded.

• Geomechanics and Engineering
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• v.19 no.2
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• pp.127-139
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• 2019

Available methods to determine the ultimate bearing capacity of shallow foundations may not be accurate enough owing to the complicated failure mechanism and diversity of the underlying soils. Accordingly, applying new methods of artificial intelligence can improve the prediction of the ultimate bearing capacity. The M5' model tree and the genetic programming are two robust artificial intelligence methods used for prediction purposes. The model tree is able to categorize the data and present linear models while genetic programming can give nonlinear models. In this study, a combination of these methods, called the M5'-GP approach, is employed to predict the ultimate bearing capacity of the shallow foundations, so that the advantages of both methods are exploited, simultaneously. Factors governing the bearing capacity of the shallow foundations, including width of the foundation (B), embedment depth of the foundation (D), length of the foundation (L), effective unit weight of the soil (${\gamma}$) and internal friction angle of the soil (${\varphi}$) are considered for modeling. To develop the new model, experimental data of large and small-scale tests were collected from the literature. Evaluation of the new model by statistical indices reveals its better performance in contrast to both traditional and recent approaches. Moreover, sensitivity analysis of the proposed model indicates the significance of various predictors. Additionally, it is inferred that the new model compares favorably with different models presented by various researchers based on a comprehensive ranking system.