• International Journal of Internet, Broadcasting and Communication
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• v.8 no.3
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• pp.12-24
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• 2016

Recently, as the price per bit is decreasing at a fast rate, flash memory is considered to be used as primary storage of large-scale database systems. Although flash memory shows off its high speeds of page reads, however, it has a problem of noticeable performance degradation in the presence of increasing update workloads. When updates are requested for pages with random page IDs, in particular, the shortcoming of flash tends to impair significantly the overall performance of a flash-based database system. Therefore, it is important to have a way to efficiently update the B+-tree, when it is stored in flash storage. This is because most of updates in the B+-tree arise at leaf nodes, whose page IDs are in random. In this light, we propose a new flash B+-tree that stores up-to-date versions of leaf nodes in sibling-leaf blocks (SLBs), while updating them. The use of SLBs improves the update performance of B-trees and provides the mechanism for fast key range searches. To verify the performance advantages of the proposed flash B+-tree, we developed a mathematical performance evaluation model that is suited for assessing B-tree operations. The performance comparisons from it show that the proposed flash B+-tree provides faster range searches and reduces more than 50% of update costs.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.293-301
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• 2012

Quantile regression proposed by Koenker and Bassett (1978) is a statistical technique that estimates conditional quantiles. The advantage of using quantile regression is the robustness in response to large outliers compared to ordinary least squares(OLS) regression. A regression tree approach has been applied to OLS problems to fit flexible models. Loh (2002) proposed the GUIDE algorithm that has a negligible selection bias and relatively low computational cost. Quantile regression can be regarded as an analogue of OLS, therefore it can also be applied to GUIDE regression tree method. Chaudhuri and Loh (2002) proposed a nonparametric quantile regression method that blends key features of piecewise polynomial quantile regression and tree-structured regression based on adaptive recursive partitioning. Lee and Lee (2006) investigated wage determinants in the Korean labor market using the Korean Labor and Income Panel Study(KLIPS). Following Lee and Lee, we fit three kinds of quantile regression tree models to KLIPS data with respect to the quantiles, 0.05, 0.2, 0.5, 0.8, and 0.95. Among the three models, multiple linear piecewise quantile regression model forms the shortest tree structure, while the piecewise constant quantile regression model has a deeper tree structure with more terminal nodes in general. Age, gender, marriage status, and education seem to be the determinants of the wage level throughout the quantiles; in addition, education experience appears as the important determinant of the wage level in the highly paid group.

• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.137-140
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• 2000

In this paper, we propose as new algorithm for the selection of key frames in a given video. For the selected key frames to be well defined, the selected key frames need to spread out on the whole temporal domain of the given video and guaranteed not to be duplicate. For this purpose, we take the first frame of each shot of the video as the candidate key frame to represent the video. To reduce the overall processing time, we eliminate some candidate key frames which are visually indistinct in the histogram difference. The key frames are then selected using a clustering processing based on the singly linked hierarchical tree. To make the selected key frames be distributed evenly on the whole video, the deviation and time difference between the selected key frames are used. The simulation results demonstrate that our method provides the better performance compared with previous methods.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.699-712
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• 2016

Recently, greedy algorithm has received much attention as a cost-effective means to reconstruct the sparse signals from compressed measurements. Much of previous work has focused on the investigation of a single candidate to identify the support (index set of nonzero elements) of the sparse signals. Well-known drawback of the greedy approach is that the chosen candidate is often not the optimal solution due to the myopic decision in each iteration. In this paper, we propose a tree search based sparse signal recovery algorithm referred to as the tree search matching pursuit (TSMP). Two key ingredients of the proposed TSMP algorithm to control the computational complexity are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In numerical simulations of Internet of Things (IoT) environments, it is shown that TSMP outperforms conventional schemes by a large margin.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.873-884
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• 2004

This research provides the solution of defect problem and detection of defect and its causes that happen on software development. For developing a reliable software, a key factor is to find and manage defects that are during software development. Based on defect items analysis, we understand associated relation between defects and design defect opportunity tree. Developing the similar project, we can estimate defect and prepare to solve defect by using defect management opportunity tree.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.714-727
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• 2015

The hierarchical structure in networks is widely applied in many practical scenarios especially in some emergency cases. In this paper, we focus on a tree network with and without packet loss where one source sends data to n destinations, through m relay nodes employing random linear network coding (RLNC) over a Galois field in parallel transmission systems. We derive closed-form probability expressions of successful decoding at a destination node and at all destination nodes in this multicast scenario. For the convenience of computing, we also propose an upper bound for the failure probability. We then investigate the impact of the major parameters, i.e., the size of finite fields, the number of internal nodes, the number of sink nodes and the channel failure probability, on the decoding performance with simulation results. In addition, numerical results show that, under a fixed exact decoding probability, the required field size can be minimized. When failure decoding probabilities are given, the operation is simple and its complexity is low in a small finite field.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.263-266
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• 1995

Comtputer Aided Process Planning(CAPP) has been emerged as playing a key role in Computer Integrated Manufactunng(CIM) as the most critical link to integrate CAD and CAM. A modified variant CAPP system based on process planning rule base is developed in this paper. This CAPP system generates process plans automatically according to the GT code data provided as input. In order to execute process planning, various process planning rules are constructed in the form of decision tree and the inference engine that extracts the process plan based on the tree-structured rules are implemented.

• Korean Management Science Review
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• v.23 no.1
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• pp.75-85
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• 2006

The rectilinear Steiner tree problem (RSTP) is to find a minimum-length rectilinear interconnection of a set of terminals in the plane. It is well known that the solution to this problem will be the minimal spanning tree(MST) on some set Steiner points. The RSTP is known to be NP-complete. The RSTP has received a lot of attention in the literature and heuristic and optimal algorithms have been proposed. A key performance measure of the algorithm for the RSTP is the reduction rate that is achieved by the difference between the objective value of the RSTP and that of the MST without Steiner points. A hybrid evolutionary strategy on RSTP based upon nodes set is presented. The computational results show that the hybrid evolutionary strategy is better than the previously proposed other heuristic. The average reduction rate of solutions from the evolutionary strategy is about 11.14%, which is almost similar to that of optimal solutions.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.51-53
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• 2015

In this paper, we propose an efficient sparse signal recovery algorithm referred to as the matching pursuit with a tree pruning (TMP). Two key ingredients of TMP are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In our analysis, we show that the sparse signal is accurately reconstructed when the sensing matrix satisfies the restricted isometry property. In our simulations, we confirm that TMP is effective in recovering sparse signals and outperforms conventional sparse recovery algorithms.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.1-3
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• 2015

In this paper, we propose a sparse signal reconstruction method referred to as the matching pursuit with a pruning-based tree search (PTS-MP). Two key ingredients of PTS-MP are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In our simulations, we confirm that PTS-MP is effective in recovering sparse signals and outperforms conventional sparse recovery algorithms.