• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.207-216
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• 2005

A binary segmentation procedure is a simple recursive approach to detect clusters and provide inferences for the study space when the shape of the clusters and the number of clusters are unknown. The procedure involves a sequence of nested hypothesis tests of a single cluster versus a pair of distinct clusters. The size and the shape of the clusters evolve as the procedure proceeds. The procedure allows for various growth clusters and for arbitrary baseline densities which govern the form of the hypothesis tests. A real tree data is used to highlight the procedure.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2596-2604
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• 1997

There are two kinds of backends in ACK:code generator(full-fledged backend) and code expander(fast backend). Code generators generate target code using string pattern matching and code expanders generate target code using macro expansion. ACK translates EM to SPARC code using code expander. The corresponding SPARC code sequences for a EM code are generated and then push-pop optimization is performed. But, there is the problem of maintaining hybrid stack. And code expander is not considered to passes parameters of a procedure call through register windows. The purpose of this paper is to improve SPARC code quality. We suggest a method of SPARC cod generation using EM tree. Our method is divided into two phases:EM tree building phase and code expansion phase. The EM tree building phase creates the EM tree and code expansion phase translates it into SPARC code. EM tree is designed to pass parameters of a procedure call through register windows. To remove hybrid stack, we extract an additional information from EM code. We improved many disadvantages that arise from code expander in ACK.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.3
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• pp.17-28
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• 2008

The Steiner arborescence problem is known to be NP-hard. The objective of this problem is to find a minimal Steiner tree which starts from a designated node and spans all given terminal nodes. This paper proposes a method based on a two-step procedure to solve this problem efficiently. In the first step, graph reduction rules eliminate useless nodes and arcs which do not contribute to make an optimal solution. In the second step. ant colony algorithm with use of Prim's algorithm is used to solve the Steiner arborescence problem in the reduced graph. The proposed method based on a two-step procedure is tested in the five test problems. The results show that this method finds the optimal solutions to the tested problems within 50 seconds. The algorithm can be applied to undirected Steiner tree problems with minor changes. 18 problems taken from Beasley are used to compare the performances of the proposed algorithm and Singh et al.'s algorithm. The results show that the proposed algorithm generates better solutions than the algorithm compared.

• Journal of KIISE:Software and Applications
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• v.32 no.8
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• pp.752-758
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• 2005

The transportation problem (TP) is known as one of the important problems in Industrial Engineering and Operational Research (IE/OR) and computer science. When the problem is associated with additional fixed cost for establishing the facilities or fulfilling the demand of customers, then it is called fixed charge transportation problem (fcTP). This problem is one of NP-hard problems which is difficult to solve it by traditional methods. This paper aims to show the application of spanning-tree based Genetic Algorithm (GA)approach for solving nonlinear fixed charge transportation problem. Our new idea lies on the GA representation that includes the feasibility criteria and repairing procedure for the chromosome. Several numerical experimental results are presented to show the effectiveness of the proposed method.

• Industrial Engineering and Management Systems
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• v.10 no.4
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• pp.272-278
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• 2011

Decision tree as a classification tool is being used successfully in many areas such as medical diagnosis, customer churn prediction, signal detection and so on. The main advantage of decision tree classifiers is their capability to break down a complex structure into a collection of simpler structures, thus providing a solution that is easy to interpret. Since decision tree is a top-down algorithm using a divide and conquer induction process, there is a risk of reaching a local optimal solution. This paper proposes a procedure of optimally determining thresholds of the chosen variables for a decision tree using an adaptive particle swarm optimization (APSO). The proposed algorithm consists of two phases. First, we construct a decision tree and choose the relevant variables. Second, we find the optimum thresholds simultaneously using an APSO for those selected variables. To validate the proposed algorithm, several artificial and real datasets are used. We compare our results with the original CART results and show that the proposed algorithm is promising for improving prediction accuracy.

• Computers and Concrete
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• v.17 no.6
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• pp.761-786
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• 2016

This paper proposes an automated procedure for optimum seismic design of reinforced concrete (RC) frame structures. This procedure combines a smart pre-processing using a Tree Classification Method (TCM) and a nonlinear optimization technique. First, the TCM automatically creates sections database and assigns sections to structural members. Subsequently, a real valued model of Particle Swarm Optimization (PSO) algorithm is employed in solving the optimization problem. Numerical examples on design optimization of three low- to high-rise RC frame structures under earthquake loads are presented with and without considering strong column-weak beam (SCWB) constraint. Results demonstrate the effectiveness of the TCMin seismic design optimization of the structures.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.365-370
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• 1997

In this paper we suggest a development of surface defect inspection algorithms for cold mill strip using tree structure. The defects which exist in a surface of cold mill strip have a scattering or singular distribution. This paper consists of preprocessing, feature extraction and defect classification. By preprocessing, the binarized defect image is achieved. In this procedure, Top-hit transform, adaptive thresholding, thinning and noise rejection are used. Especially, Top-hit transform using local min/max operation diminishes the effect of bad lighting. In feature extraction, geometric, moment, co-occurrence matrix, histogram-ratio features are calculated. The histogram-ratio feature is taken from the gray-level image. For the defect classification, we suggest a tree structure of which nodes are multilayer neural network clasifiers. The proposed algorithm reduced error rate comparing to one stage structure.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.413-418
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• 1992

This paper presents a tree search technique to solve the dynamic control problems. To illustrate the proposed procedure, the swinging control of a pendulum carried by a motor-driven cast is discussed as an example. Since the control system is of two degrees and the control problem is a nonlinear one, it is difficult to determine a swinging control rule analytically. However, by means of the proposed tree search approach, the problem can be solved in a relatively easy way. Some numerical calculations axe performed to verify the methodology. The result of the study shows that the proposed tree search technique is suitable for the dynamic control problems, in particular, for the complicated nonlinear dynamic control problem.

• Korean Management Science Review
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• v.26 no.1
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• pp.65-76
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• 2009

The undirected Steiner tree problem in graphs is known to be NP-hard. The objective of this problem is to find a shortest tree containing a subset of nodes, called terminal nodes. This paper proposes a method based on a two-step procedure to solve this problem efficiently. In the first step. graph reduction rules eliminate useless nodes and edges which do not contribute to make an optimal solution. In the second step, a max-min ant colony optimization combined with Prim's algorithm is developed to solve the reduced problem. The proposed algorithm is tested in the sets of standard test problems. The results show that the algorithm efficiently presents very correct solutions to the benchmark problems.

• Journal of the Korea Safety Management & Science
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• v.4 no.1
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• pp.37-47
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• 2002

Risk analysis is a formal deductive procedure for determining combinations of component failures and human errors that could result in the occurrence of specified undesired events at the system level. This method can be used to analyze the vast majority of industrial system reliability problems. This study deals with the application of knowledge-engineering and a methodology for the assessment ＆ measurement of reliability, availability, maintainability, and safety of industrial systems using FTA(fault tree analysis), A fuzzy methodology for fault-tree evaluation seems to be an alternative solution to overcome the drawbacks of the conventional approach (insufficient Information concerning the relative frequencies of hazard events). To improve the quality of results, the membership functions must be approximated based on heuristic considerations, The purpose of this study is to describe the knowledge engineering approach, directed to integrate the various sources of knowledge involved in a FTA.