• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.1
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• pp.9-15
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• 2010

As catenary supply electric power directly to the railway system, it is very important to prevent an accident of a catenary for appropriate train operation. This paper proposed the assessment the outage data for "British Catenary Safety Analysis Report" and Korean data to compare the reliability of the railway system. The analyzed data were applied to Event Tree and Fault Tree algorithm to calculate the reliability indices of railway system. Event tree is created and gate results of fault tree analysis are used as the source of event tree probabilities. Fault tree represents the interaction of failures and basic events within a system. Event Tree and Fault Tree analysis result is helpful to assess the reliability to interpreted. The reliability indices can be used to determine the equipment to be replaced for the entire system reliability improvement.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.4
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• pp.441-448
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• 2010

This paper estimates the tree height using Airborne LiDAR that is obtained for each season to analyze its influence based on a canopyclosure and data fusion. The tree height was estimated by extracting the First Return (RF) from the tree and the Last Return (LR) from the surface of earth to assume each tree via image segmentation and to obtain the height of each tree. Each data on tree height that is collected from seasonal data and the result of tree height acquired from the data fusion were compared. A tree height measuring device was used to measure on site and its accuracy was compared. Also, its applicability on the result of fused data that is obtained through the Airborne LiDAR is examined. As a result of the experiment, the result of image segmentation for an individual tree was closer to the result of site study for 1 meter interval when compared to the 0.5 meter interval of point cloud. In case of the tree height, the application of fused data enables a closer site measurement result than the application of data for each season.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.332-335
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• 2008

In this paper, based on H-tree, which is proposed as the basic data cube structure for multi-dimensional data stream analysis, we have done some analysis. We find there are a lot of redundant nodes in H-tree, and the tree-build method can be improved for saving not only memory, but also time used for inserting tuples. Also, to facilitate more fast and large amount of data stream analysis, which is very important for stream research, H^*-tree is designed and developed. Our performance study compare the proposed H^*-tree and H-tree, identify that H^*-tree can save more memory and time during inserting data stream tuples.

• Journal of the Korean Institute of Landscape Architecture
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• v.21 no.3
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• pp.89-98
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• 1993

It is necessary for the park tree manager to have the current information about the status of trees, which can help him with right decisions. However, there are many problems in the existing management method such as huge amount of data, tedious work, and the difficult update work due to the lack of necessary data or the inappropriate data record and management method. The sole use of database management system(DBMS) cannot slove these problems because it cannot handle graphic data based on the locational information. So, it is imperative for the park manager to have locational data as well as attribute data of the park tree concerned. Therefore, the purpose of this study is to develop the personal computer-based, user friendly park tree management information system, which deals with attribute data(DBMS) and graphic data(using the CAD) together within the integrated environment. The park tree management information system developed in this study provides a complete operating environment for data input, update, query, delete, and retrieve. The major advantages of this system are as follows: 1) To search the location and distribution of trees. 2) To record, store, and manage data easily. 3) When the manager is changed, delivery of the park tree work is convenient. 4) The system can help the manager with the correct information for the efficient park tree management.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.840-849
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• 2013

This paper presents a statistical approach to analyze electrical tree inception voltage, electrical tree breakdown voltage and tree breakdown time of unsaturated polyester resin subjected to AC voltage. The aim of this work was to show that Weibull and lognormal distribution may not be the most suitable distributions for analysis of electrical treeing data. In this paper, an investigation of statistical distributions of electrical tree inception voltage, electrical tree breakdown voltage and breakdown time data was performed on 108 leaf-like specimen samples. Revelations from the test results showed that Johnson SB distribution is the best fit for electrical tree inception voltage and tree breakdown time data while electrical tree breakdown voltage data is best suited with Wakeby distribution. The fitting step was performed by means of Anderson-Darling (AD) Goodness-of-fit test (GOF). Based on the fitting results of tree inception voltage, tree breakdown time and tree breakdown voltage data, Johnson SB and Wakeby exhibit the lowest error value respectively compared to Weibull and lognormal.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.30-38
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• 2004

In many scientific and commercial applications such as Earth Observation System (EOSDIS) and mobile Phone services tracking a large number of clients, it is a daunting task to archive and index ever increasing volume of complex data that are continuously added to databases. To efficiently manage multidimensional data in scientific and data warehousing environments, R-tree based index structures have been widely used. In this paper, we propose a scalable technique called seeded clustering that allows us to maintain R-tree indexes by bulk insertion while keeping pace with high data arrival rates. Our approach uses a seed tree, which is copied from the top k levels of a target R-tree, to classify input data objects into clusters. We then build an R-tree for each of the clusters and insert the input R-trees into the target R-tree in bulk one at a time. We present detailed algorithms for the seeded clustering and bulk insertion as well as the results from our extensive experimental study. The experimental results show that the bulk insertion by seeded clustering outperforms the previously known methods in terms of insertion cost and the quality of target R-trees measured by their query performance.

• Journal of Korea Multimedia Society
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• v.12 no.7
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• pp.962-970
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• 2009

This paper presents a data gathering and scheduling scheme for wireless sensor networks. We use a data gathering tree for sending the data from the sensor node to the base station. For an energy efficient operation of the sensor networks in a distributed manner, a time tree is built in order to reduce the collision probability and to minimize the total energy required to send data to the base station. A time tree is a data gathering tree where the base station is the root and each sensor node is either a relaying or a leaf node of the tree. Each tree operates in a different time schedule with possible different activation rate. Through the simulation, we found that the proposed scheme that uses time trees shows better characteristics in energy and data arrival rate when compared with other schemes such as SMAC and DMAC.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.164-173
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• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, data reduction and variable screening, category merging, etc. We analyze waste database united with local information using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

• Spatial Information Research
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• v.19 no.2
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• pp.47-56
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• 2011

Typical tree-based spatial index structures are divided into a data-partitioning index structure such as R-Tree and a space-partitioning index structure such as KD-Tree. In recent years, researches on hybrid index structures combining advantages of these index structures have been performed extensively. However, because the split boundary extension of the node to which a new spatial object is inserted may extend split boundaries of other neighbor nodes in existing researches, overlaps between nodes are increased and the query processing cost is raised. In this paper, we propose a hybrid index structure, called SQR-Tree that can support efficient processing of spatial queries to solve these problems. SQR-Tree is a combination of SQ-Tree(Spatial Quad- Tree) which is an extended Quad-Tree to process non-size spatial objects and R-Tree which actually stores spatial objects associated with each leaf node of SQ-Tree. Because each SQR-Tree node has an MBR containing sub-nodes, the split boundary of a node will be extended independently and overlaps between nodes can be reduced. In addition, a spatial object is inserted into R-Tree in each split data space and SQ-Tree is used to identify each split data space. Since only R-Trees of SQR-Tree in the query area are accessed to process a spatial query, query processing cost can be reduced. Finally, we proved superiority of SQR-Tree through experiments.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.673-682
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• 2009

XML data are widely used for data representation and exchange on the Web and the data type is an continuous stream in ubiquitous environment. Therefore there are some mining researches related to the extracting of frequent structures and the efficient query processing of XML stream data. In this paper, we propose a mining method to extract frequent structures of XML stream data in recent window based on the sliding window. XML stream data are modeled as a tree set, called XFP_tree and we quickly extract the frequent structures over recent XML data in the XFP_tree.