• Journal of IKEEE
• /
• v.22 no.3
• /
• pp.850-853
• /
• 2018

In this paper, we propose a decoding method using a balanced binary tree and a canonical Huffman tree for efficient decoding of Huffman codes. The balanced binary tree scheme reduces the number of searches by lowering the height of the tree and binary search. However, constructing a tree based on the value of the code instead of frequency of symbol is a drawback of the balanced binary tree. In order to overcome these drawbacks, a balanced binary tree is reconstructed according to the occurrence probability of symbols at each level of the tree and binary search is performed for each level. We minimize the number of searches using a canonical Huffman tree to find level of code to avoid searching sequentially from the top level to bottom level.

• Journal of Korea Spatial Information System Society
• /
• v.11 no.3
• /
• pp.55-62
• /
• 2009

Location based Services in the ubiquitous computing environment, namely u-LBS, use very large and skewed spatial objects that are closely related to locational information. It is especially essential to achieve fast search, which is looking for POI(Point of Interest) related to the location of users. This paper examines how to search large and skewed POI efficiently in the u-LBS environment. We propose the Dynamic-level Grid based R-Tree(DGR-Tree), which is an index for point data that can reduce the cost of stationary POI search. DGR-Tree uses both R-Tree as a primary index and Dynamic-level Grid as a secondary index. DGR-Tree is optimized to be suitable for point data and solves the overlapping problem among leaf nodes. Dynamic-level Grid of DGR-Tree is created dynamically according to the density of POI. Each cell in Dynamic-level Grid has a leaf node pointer for direct access with the leaf node of the primary index. Therefore, the index access performance is improved greatly by accessing the leaf node directly through Dynamic-level Grid. We also propose a K-Nearest Neighbor(KNN) algorithm for DGR-Tree, which utilizes Dynamic-level Grid for fast access to candidate cells. The KNN algorithm for DGR-Tree provides the mechanism, which can access directly to cells enclosing given query point and adjacent cells without tree traversal. The KNN algorithm minimizes sorting cost about candidate lists with minimum distance and provides NEB(Non Extensible Boundary), which need not consider the extension of candidate nodes for KNN search.

• Journal of KIISE
• /
• v.42 no.1
• /
• pp.86-92
• /
• 2015

A level set tree provides a useful representation of a multidimensional density function. Visualizing the data structure as a tree offers many advantages for data analysis and clustering. In this paper, we present a level set tree estimation algorithm for use with a set of data points. The proposed algorithm creates a level set tree from a family of level sets estimated over a whole range of levels from zero to infinity. Instead of estimating density function then thresholding, we directly estimate the density level sets using one-class support vector machines (OC-SVMs). The level set estimation is facilitated by the OC-SVM solution path algorithm. We demonstrate the proposed level set tree algorithm on benchmark data sets.

• 한국정보통신설비학회:학술대회논문집
• /
• 2009.08a
• /
• pp.131-135
• /
• 2009

This paper is the introduction of our work on distributed load scheduling in single-level tree network. In this paper, we derive a new calculation model in single-level tree network and show a closed-form formulation of the time for computation system. There are so many examples of the application of this technology such as distributed database, biology computation on genus, grid computing, numerical computing, video and audio signal processing, etc.

• Journal of KIISE:Databases
• /
• v.30 no.5
• /
• pp.521-531
• /
• 2003

Multi-level geographic data can be mainpulated by a window query such as a zoom operation. In order to handle multi-level geographic data efficiently, a spatial indexing method supporting a window query is needed. However, the conventional spatial indexing methods are not efficient to access multi-level geographic data quickly. To solve it, other a few spatial indexing methods for multi-level geographic data are known. However these methods do not support all types of multi-level geographic data. This paper presents a new efficient spatial indexing method, the MLR-tree for window query of multi-level geographic data. The MLR-tree offers both high search performance and no data redundancy. Experiments show them. Moreover, the MLR-tree supports all types of multi-level geographic data.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.19 no.6
• /
• pp.1-8
• /
• 1982

In this paper a new analytic method for Ladder networks by weighted tree is proposed. In contrast to conventional tree concept that represents only information structure, in this paper, a tree with hierarchical structure is established by giving wei체t of impedance Z and admittance Y to branch and representing each node of its branch as a pair of voltage and current. Then, by defining generation level from tree structure and by parsing between standand level and arbitrary level, driving point impedance, transfer function and transfer impedance are simultaneously obtained instead of complex calculation method by inspection. The validity of this method is proved by the reciprocal theorem and this method is applied to four-terminal constants and the feedback network.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.2
• /
• pp.637-647
• /
• 2018

In this paper, we propose a method to search candidates of network reconfiguration to restore distribution system with distributed energy resources using a level-based tree search algorithm. First, we introduce a method of expressing distribution network with distributed energy resources for fault restoration, and to represent the distribution network into a simplified graph. Second, we explain the tree search algorithm, and introduce a method of performing the tree search on the basis of search levels, which we call a level-based tree search in this paper. Then, we propose a candidate search method for fault restoration, and explain it using an example. Finally, we verify the proposed method using computer simulations.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.4
• /
• pp.17-22
• /
• 2022

With the spread of web services, data types are becoming more diversified. In addition to the form of storing data such as images, videos, and texts, the number and form of properties and metadata expressing the data are different for each data. In order to efficiently process such unstructured data, a key-value store is widely used for state-of-the-art applications. LSM-Tree (Log Structured Merge Tree) is the core data structure of various commercial key-value stores. LSM-Tree is optimized to provide high performance for small writes by recording all write and delete operations in a log manner. However, there is a problem in that the delay time and processing speed of user requests are lowered as batches of deletion operations for expired data are inserted into the LSM-Tree as special key-value data. This paper presents a Filtered LSM-Tree (FLSM-Tree) that solves the above problem by separating the deleted key from the main tree structure while maintaining all the advantages of the existing LSM-Tree. The proposed method is implemented in LevelDB, a commercial key-value store and it shows that the read performance is improved by up to 47% in performance evaluation.

• Journal of Korean Institute of Industrial Engineers
• /
• v.42 no.2
• /
• pp.151-164
• /
• 2016

In this paper, we deal with a risk analysis for an IPS (Integrated power system) and propose a simulation model combining the fault tree and event tree in order to estimate the system availability and risk level, together. Firstly, the basic information such as operational scenarios, physical structure, safety systems is explained in order to make the fault tree and event tree of the IPS. Next, we propose a discrete-event simulation model using a next-event time advance technique to advance the simulation time. Also the state transition and activity diagrams are explained to represent the relationship between the objects. By numerical examples, the redundancy allocation is considered in order to decrease the risk level of the IPS.

• International Journal of Highway Engineering
• /
• v.16 no.4
• /
• pp.127-134
• /
• 2014

PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.