• Journal of KIISE:Databases
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• v.34 no.3
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• pp.217-231
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• 2007

Frequent disconnection is connected directly to client's cache consistency problem in Mobile Computing environment. For solving cache consistency problem, research about Invalidation Report is studied. But, existent invalidation report structure comes with increase of size of invalidation report structure and decline of cache efficiency if quantity of data become much, or quantity of updated data increases. Also, while existent method confirms whole cache, invalidation report doesn't support selective listening. This paper proposes TTCI(Timestamp Tree-based Cache Invalidation scheme) as invalidation report structure that solve problem of these existing schemes and improve efficiency. We can make TTCI using timestamp of updated data, composing timestamp tree and list ID of data in updated order. If we utilize this, each client can confirm correct information in point that become own disconnecting and increase cache utilization ratio. Also, we can pare down client's resources consumption by selective listening using tree structure. We experimented in comparison with DRCI(Dual-Report Cache Invalidation) that is existent techniques to verify such efficiency of TTCI scheme.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.113-120
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• 2024

With the advancement of information and communication technology, we can easily generate various forms of data in our daily lives. To efficiently manage such a large amount of data, systematic classification into categories is essential. For effective search and navigation, data is organized into a tree-like hierarchical structure known as a category tree, which is commonly seen in news websites and Wikipedia. As a result, various techniques have been proposed to classify large volumes of documents into the terminal nodes of category trees. However, document classification methods using category trees face a problem: as the height of the tree increases, the number of terminal nodes multiplies exponentially, which increases the probability of misclassification and ultimately leads to a reduction in classification accuracy. Therefore, in this paper, we propose a new node expansion-based classification algorithm that satisfies the classification accuracy required by the application, while enabling detailed categorization. The proposed method uses a greedy approach to prioritize the expansion of nodes with high classification accuracy, thereby maximizing the overall classification accuracy of the category tree. Experimental results on real data show that the proposed technique provides improved performance over naive methods.

• Journal of Information Technology Applications and Management
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• v.15 no.4
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• pp.21-36
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• 2008

As the great disturbance from abusing credit cards in Korea becomes stabilized, credit card companies need to interpret credit-card delinquents classification models from the viewpoint of profit. However, hit ratio which has been used as a measure of goodness of classification models just tells us how much correctly they classified rather than how much profits can be obtained as a result of using classification models. In this research, we tried to develop a new utility-based measure from the viewpoint of profit and then used this new measure to analyze two classification models(Neural Networks and Decision Tree models). We found that the hit ratio of neural model is higher than that of decision tree model, but the utility value of decision tree model is higher than that of neural model. This experiment shows the importance of utility based measure for credit-card delinquents classification models. We expect this new measure will contribute to increasing profits of credit card companies.

• Korean Journal of Environmental Biology
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• v.21 no.3
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• pp.251-256
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• 2003

Baseline data on the type, species and volume for dead trees were collected from old growth fir (Abies holophylla) forest in Sorak National Park. Though the survey was restricted to fly forest, a basic data compatible to those in other countries were collected. Besides fir, dead trees were also found in deciduous species. All the Known dead tree types were found. Average volume of dead tree in the surveyed area was 00.42 $\textrm{m}^2 \; ha^{-1}$. There seems no correlation existing between the distributions of dead tree and coarse woody debris (CWD).

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.07a
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• pp.319-322
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• 2001

The proposed system was composed of pre-processor which was executing binary/high-pass filtering and post-processor which ranged from statistic data to prediction. In post-processor work, step one was filter process of image, step two was image recognition, and step three was destruction degree/time prediction. After these processing, we could predict image of the last destruction timestamp. This research was produced variation value according to growth of tree pattern. This result showed improved correction, when this research was applied image Processing. Pre-processing step of original image had good result binary work after high pass- filter execution. In the case of using partial discharge of the image, our research could predict the last destruction timestamp. By means of experimental data, this Prediction system was acquired ${\pm}$3.2% error range.

• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.374-377
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• 2003

In this paper, a performance evaluation model of the Fat-Tree network with the multiple-buffered crossbar switches is proposed and examined. Buffered switch technique is well known to solve the data collision problem in the switch network The proposed evaluation model is developed by investigating the transfer patterns of data packets in a switch with output-buffers. Steady state probability concept is used to simplify the analyzing processes. Two important parameters of the network performance, throughput and delay, are then evaluated. To validate the proposed analysis model, the simulation is carried out on the various sizes of Fat-tree networks that use the multiple a$\times$b buffered crossbar switches. It is observed that both analysis and simulation results are match closely.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.157-159
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• 2007

The conventional hard disk has been the dominant database storage system for over 25 years. Recently, hybrid systems which incorporate the advantages of flash memory into the conventional hard disks are considered to be the next dominant storage systems to support databases for desktops and server computers. Their features are satisfying the requirements like enhanced data I/O, energy consumption and reduced boot time, and they are sufficient to hybrid storage systems as major database storages. However, we need to improve traditional index node management schemes based on B-Tree due to the relatively slow characteristics of hard disk operations, as compared to flash memory. In order to achieve this goal, we propose a new index node management scheme called FNC-Tree. FNC-Tree-based index node management enhanced search and update performance by caching data objects in unused free area of flash leaf nodes to reduce slow hard disk I/Os in index access processes.

• Journal of Environmental Impact Assessment
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• v.19 no.5
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• pp.511-525
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• 2010

Understanding of the hydraulics of flow over vegetation is very important to support the management of fluvial processes. The objective of this study is to assess the effects of hydraulic influence by tree planting in a compound channel with vegetated floodplain. This study analyzes the influence of tree planting on hydraulic features in Young-river in Munkyung city using HEC-RAS and RMA-2 model. The study results showed that there is a rise in water surface elevation and decrease in velocity near vegetated area. It is also ascertained that only negligible effects was seen within the feasible range of freeboard for the existing levees. However, as hydraulic features can vary depending on the aspect of flood inundation during each flood period, it is necessary to accumulate data through continuous data collecting.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.247-249
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• 2007

Crown parameters are important roles in tree species identification, because the canopy is the aggregate of all the crowns. However, crown measurements with spaceborne image data have remained more difficult than on aerial photographs since trees show more structural detail at higher resolutions. This recognized problem led to the initiation of the research to determine if high resolution satellite image data could be used to identify and classify single tree species. In this paper, shape parameters derived from pixel-based crown area measurements and texture features derived from GLCM parameters in QuickBird image were tested and compared for individual tree species identification. As expected, initial studies have shown that the crown parameters and the canopy texture parameters provided a differentiating method between coniferous trees and broad-leaved trees within the compartment(less than forest stand) for single extraction from spaceborne high resolution image.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.