• Nuclear Engineering and Technology
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• v.50 no.6
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• pp.854-859
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• 2018

Calculating minimal cut sets is a typical quantification method used to evaluate the top event probability for a fault tree. If minimal cut sets cannot be calculated or if the accuracy of the quantification result is in doubt, the Monte Carlo method can provide an alternative for fault tree quantification. The Monte Carlo method for fault tree quantification tends to take a long time because it repeats the calculation for a large number of samples. Herein, proposal is made to improve the quantification algorithm of a fault tree with circular logic. We developed a top-down iteration algorithm that combines the characteristics of the top-down approach and the iteration approach, thereby reducing the computation time of the Monte Carlo method.

• Journal of KIISE:Information Networking
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• v.28 no.4
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• pp.611-620
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• 2001

To meet the requirements of reliable service for various applications, a Reliable Multicast Transport Protocol should be implemented over IP Multicast where currently best-effort service is provided. Among the current researches, hierarchical tree-based mechanism has been proposed and actively studied. This mechanism is known to provide high scalability as well as reliability, but needs an additional tree configuring mechanism for building an efficient logical tree in transport layer. Bottom-up approach has been used for creating such a tree. This method has benefits from parallel tree construction for receivers, while it has some drawbacks such that it does not guarantee a loop-free tree and brings heavy message overhead during tree creation process. Therefore, this paper proposes a top-down based mechanism for constructing a control tree, which can guarantee loop-freeness by step-wise mannered tree building. From experimental simulations, it shows that the proposed mechanism has less message overhead. It is recommended that the bottom-up and the proposed top-down will be selectively used in real networks, according to the requirements of the concerned multicast applications.

• Journal of Computing Science and Engineering
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• v.4 no.2
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• pp.173-187
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• 2010

Recent technological advances in mobile communication systems have made explosive growth in the number of mobile device users worldwide. One of the most important issues in designing a mobile computing system is location management of users. The hierarchical systems had been proposed to solve the scalability problem in location management. The scalability problem occurs when there are too many users for a mobile system to handle, as the system is likely to react slow or even get down due to late updates of the location databases. In this paper, we propose a top-down clustering algorithm for hierarchical location database systems in a wireless network. A hierarchical location database system employs a tree structure. The proposed algorithm uses a top-down approach and utilizes the number of visits to each cell made by the users along with the movement information between a pair of adjacent cells. We then present a modified algorithm by incorporating the exhaustive method when there remain a few levels of the tree to be processed. We also propose a capacity constraint top-down clustering algorithm for more realistic environments where a database has a capacity limit. By the capacity of a database we mean the maximum number of mobile device users in the cells that can be handled by the database. This algorithm reduces a number of databases used for the system and improves the update performance. The experimental results show that the proposed, top-down, modified top-down, and capacity constraint top-down clustering algorithms reduce the update cost by 17.0%, 18.0%, 24.1%, the update time by about 43.0%, 39.0%, 42.3%, respectively. The capacity constraint algorithm reduces the average number of databases used for the system by 23.9% over other algorithms.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.5
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• pp.325-333
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• 2015

Vision based night-time vehicle detection has been an emerging research field in various advanced driver assistance systems(ADAS) and automotive vehicle as well as automatic head-lamp control. In this paper, we propose night-time vehicle detection method based on multi-class support vector machine(SVM) that consists of thresholding, labeling, feature extraction, and multi-class SVM. Vehicle light candidate blobs are extracted by local mean based thresholding following by labeling process. Seven geometric and stochastic features are extracted from each candidate through the feature extraction step. Each candidate blob is classified into vehicle light or not by multi-class SVM. Four different multi-class SVM including one-against-all(OAA), one-against-one(OAO), top-down tree structured and bottom-up tree structured SVM classifiers are implemented and evaluated in terms of vehicle detection performances. Through the simulations tested on road video sequences, we prove that top-down tree structured and bottom-up tree structured SVM have relatively better performances than the others.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.845-857
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• 2003

We explore the use of genetic programming to evolve decision trees directly for classification problems with both discrete and continuous predictors. We demonstrate that the derived hypotheses of standard algorithms can substantially deviated from the optimum. This deviation is partly due to their top-down style procedures. The performance of the system is measured on a set of real and simulated data sets and compared with the performance of well-known algorithms like CHAID, CART, C5.0, and QUEST. Proposed algorithm seems to be effective in handling problems caused by top-down style procedures of existing algorithms.

• ETRI Journal
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• v.23 no.3
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• pp.111-120
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• 2001

For scalable multicast transport, one of the promising approaches is to employ a control tree known as acknowledgement (ACK) tree which can be used to convey information on reliability and session status from receivers to a root sender. The existing tree configuration has focused on a 'bottom-up' scheme in which ACK trees grow from leaf receivers toward a root sender. This paper proposes an alternative 'top-down' configuration where an ACK tree begins at the root sender and gradually expands by including non-tree nodes into the tree in a stepwise manner. The proposed scheme is simple and practical to implement along with multicast transport protocols. It is also employed as a tree configuration in the Enhanced Communications Transport Protocol, which has been standardized in the ITU-T and ISO/IEC JTC1. From experimental simulations, we see that the top-down scheme provides advantages over the existing bottom-up one in terms of the number of control messages required for tree configuration and the number of tree levels.

• 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.

• Proceedings of the Korean Reliability Society Conference
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• 2001.06a
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• pp.257-263
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• 2001

In the analysis of product reliability, the fault tree is widely used since it shows the interrelations of the faults that lead to the product fault. A top-down approach based on experts’ experience is commonly used in the fault tree construction and the trees often take different forms depending on the intent of the analyst. In this work it is studied how to construct fault trees with the utilization of function trees obtained from analyzing the functions and sub-functions of products in order to suggest a generic way of fault tree construction. The function tree of a product is obtained by analyzing basic functions comprising the product main function in a bottom-up approach so that it enables to construct an objective fault tree. The fault tree for a scroll compressor is shown as an example.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.943-946
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• 1999

In this paper, we present a new clock routing algorithm which minimizes total wirelength under any given path-length skew bound. The algorithm onstructs a bounded-skew tree(BST) in two steps:(ⅰ) a bottom-up phase to construct a binary tree of shortest-distance feasible regions which represent the loci of possible placements of clock entry points, and (ⅱ) a top-down phase to determine the exact locations of clock entry points. Experimental results show that our clock routing algorithm, named BST/DME, can produce a set of solutions with skew and wirelength trade-off.

• Korean Management Science Review
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• v.23 no.1
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• pp.63-74
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• 2006

Decision tree induction algorithm is one of the most widely used methods in classification problems. However, they could be trapped into a local minimum and have no reasonable means to escape from it if tree algorithm uses top-down search algorithm. Further, if irrelevant or redundant features are included in the data set, tree algorithms produces trees that are less accurate than those from the data set with only relevant features. We propose a hybrid algorithm to generate decision tree that uses genetic programming with sequentially selected features. Correlation-based Feature Selection (CFS) method is adopted to find relevant features which are fed to genetic programming sequentially to find optimal trees at each iteration. The new proposed algorithm produce simpler and more understandable decision trees as compared with other decision trees and it is also effective in producing similar or better trees with relatively smaller set of features in the view of cross-validation accuracy.