• Spatial Information Research
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• v.19 no.2
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• pp.71-76
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• 2011

In this paper, for using emerging patterns to define and analyze the significant difference of safe and non-safe power load lines, and identify which line is potentially non-safe, we proposed an incremental TFP-tree algorithm for mining emerging patterns that can search efficiently within limitation of memory. Especially, the concept of pre-infrequent patterns pruning and use of two different minimum supports, made the algorithm possible to mine most emerging patterns and handle the problem of mining from incrementally increased, large size of data sets such as power consumption data.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.2
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• pp.11-22
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• 1992

Up to now, most researches on production automation have concentrated on local automation, e. g. CAD, CAM, robotics, etc. However, to achieve total automation it is required to link each local modules such as CAD, CAM into a unified and integrated system. One such missing link is between CAD and computer vision system. This thesis is an attempt to link the gap between CAD and computer vision system. In this paper, we propose algorithms that carry out edge detection, thinning and pruning from the image data of manufactured parts, which are obtained from video camera and then transmitted to computer. We also propose a feature extraction and surface determination algorithm which extract informations from the image data. The informations are compatible to IGES CAD data. In addition, we suggest a methodology to reduce search efforts for CAD data bases. The methodology is based on graph submatching algorithm in GEFG(Generalized Edge Face Graph) representation for each part.

• ETRI Journal
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• v.43 no.6
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• pp.1024-1037
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• 2021

Mining high utility itemsets (HUIs) from transaction databases considers such factors as the unit profit and quantity of purchased items. Two-phase tree-based algorithms transform a database into compressed tree structures and generate candidate patterns through a recursive pattern-growth procedure. This procedure requires a lot of memory and time to construct conditional pattern trees. To address this issue, this study employs two compressed tree structures, namely, Utility Count Tree and String Utility Tree, to enumerate valid patterns and thus promote fast utility computation. Furthermore, the study presents an algorithm called single-phase utility computation (SPUC) that leverages these two tree structures to mine HUIs in a single phase by incorporating novel pruning strategies. Experiments conducted on both real and synthetic datasets demonstrate the superior performance of SPUC compared with IHUP, UP-Growth, and UP-Growth+algorithms.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.175-177
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• 2018

This paper proposes an algorithm to speed up block structure partition of quad tree plus binary tree (QTBT) in Joint Exploration Test Model (JEM) encoder. The proposed fast encoding of QTBT block partition employs three spatially neighbor coded blocks, such as left, top-left, and top of current block, to early terminate QTBT block structure pruning. The propose algorithm is organized based on statistical similarity of those spatially neighboring blocks, such as block depths and coded block types, which are coded with overlapped block motion compensation (OBMC) and adaptive multi transform (AMT). The experimental results demonstrate about 30% encoding time reduction with 1.3% BD-rate loss on average compared to the anchor JEM-7.1 software under random access configuration.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.439-448
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• 1996

Extraction and selection of the informative features play a central role in pattern recognition. This paper describes a modified back-propagation algorithm that performs selection of the informative features and trains a neural network simultaneously. The algorithm is mainly composed of three repetitive steps : training, connection pruning, and input unit elimination. Afer initial training, the connections that have small magnitude are first pruned. Any unit that has a small number of connections to the hidden units is deleted,which is equivalent to excluding the feature corresponding to that unit.If the error increases,the network is retraned,again followed by connection pruning and input unit elimination.As a result,the algorithm selects the most im-portant features in the measurement space without a transformation to another space.Also,the selected features are the most-informative ones for the classification,because feature selection is tightly coupled with the classifi-cation performance.This algorithm helps avoid measurement of redundant or less informative features,which may be expensive.Furthermore,the final network does not include redundant parameters,i.e.,weights and biases,that may cause degradation of classification performance.In applications,the algorithm preserves the most informative features and significantly reduces the dimension of the feature vectors whiout performance degradation.

• Journal of Korea Game Society
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• v.18 no.5
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• pp.59-66
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• 2018

In this paper, one of the traditional game, Gonu game, is implemented and experimented. The Minimax algorithm was applied as a technique to implement the Gonu game. We proposed an evaluation function to implement game in Minimax algorithm. We analyze the efficiency of algorithm for alpha beta pruning to improve the performance after implementation of Gonu game. Weights were analyzed for optimal analysis that affected the win or loss of the game. For the weighting analysis, a competition of human and computer was performed. We also experimented with computer and computer. As a result, we proposed a weighting value for optimal attack and defense.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3219-3236
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• 2012

A tree routing structure is often adopted for many-to-one data gathering and aggregation in sensor networks. For real-time scenarios, considering lossy wireless links, it is an important issue how to construct a maximum-lifetime data gathering tree with delay constraint. In this work, we study the problem of lifetime-preserving and delay-constrained tree construction in unreliable sensor networks. We prove that the problem is NP-complete. A greedy approximation algorithm is proposed. We use expected transmissions count (ETX) as the link quality indicator, as well as a measure of delay. Our algorithm starts from an arbitrary least ETX tree, and iteratively adjusts the hierarchy of the tree to reduce the load on bottleneck nodes by pruning and grafting its sub-tree. The complexity of the proposed algorithm is $O(N^4)$. Finally, extensive simulations are carried out to verify our approach. Simulation results show that our algorithm provides longer lifetime in various situations compared to existing data gathering schemes.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.537-545
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• 2009

In this paper, we proposed an effective multicast causal order algorithm with hand-off protocol for mobile networks. Since the size of control informations needed to enforce message transfer order has much influence on the performance of hand-off and message transfer in mobile networks, size of control information need to be minimized. We reduced the size of control information by analyzing all the valid communication patterns and pruning redundant information not required to enforce causal order as early as possible, and used hand-off protocol which requires minimal amount of control information to be transferred. By simulation, we found that the proposed algorithm showed better performance than other existing algorithms.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.406-420
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• 2020

In order to make up the deficiencies of the existing research results which cannot effectively deal with the nearest neighbor query based on the line segments in obstacle space, the k nearest neighbor query method of line segment in obstacle space is proposed and the STA_OLkNN algorithm under the circumstance of static obstacle data set is put forward. The query process is divided into two stages, including the filtering process and refining process. In the filtration process, according to the properties of the line segment Voronoi diagram, the corresponding pruning rules are proposed and the filtering algorithm is presented. In the refining process, according to the relationship of the position between the line segments, the corresponding distance expression method is put forward and the final result is obtained by comparing the distance. Theoretical research and experimental results show that the proposed algorithm can effectively deal with the problem of k nearest neighbor query of the line segment in the obstacle environment.

• ETRI Journal
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• v.44 no.5
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• pp.746-758
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• 2022

This paper focuses on a mobile edge-computing-enabled heterogeneous network. A battery level-aware task-scheduling framework is proposed to improve the energy efficiency and prolong the operating hours of battery-powered mobile devices. The formulated optimization problem is a typical mixed-integer nonlinear programming problem. To solve this nondeterministic polynomial (NP)-hard problem, a decomposition-based task-scheduling algorithm is proposed. Using an alternating optimization technology, the original problem is divided into three subproblems. In the outer loop, task offloading decisions are yielded using a pruning search algorithm for the task offloading subproblem. In the inner loop, closed-form solutions for computational resource allocation subproblems are derived using the Lagrangian multiplier method. Then, it is proven that the transmitted power-allocation subproblem is a unimodal problem; this subproblem is solved using a gradient-based bisection search algorithm. The simulation results demonstrate that the proposed framework achieves better energy efficiency than other frameworks. Additionally, the impact of the battery level-aware scheme on the operating hours of battery-powered mobile devices is also investigated.