• Journal of KIISE:Databases
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• v.31 no.6
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• pp.699-709
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• 2004

XML is increasingly important in data exchange and information management. Starting point for retrieving the structure and integrating the documents efficiently is clustering the documents that have similar structure. The reason is that we can retrieve the documents more flexible and faster than the method treating the whole documents that have different structure. Therefore, in this paper, we propose the similar structure-based incremental clustering method useful for retrieving the structure of XML documents and integrating them. As a novel method, we use a clustering algorithm for transactional data that facilitates the large number of data, which is quite different from the existing methods that measure the similarity between documents, using vector. We first extract the representative structures of XML documents using sequential pattern algorithm, and then we perform the similar structure based document clustering, assuming that the document as a transaction, the representative structure of the document as the items of the transaction. In addition, we define the cluster cohesion and inter-cluster similarity, and analyze the efficiency of the Proposed method through comparing with the existing method by experiments.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.296-302
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• 2022

This paper proposes a center of plantar foot pressure (CoP) trajectory estimation method based on Gaussian process regression, with the aim to show robust results regardless of the regions and numbers of FSRs of the insole sensor. This method can bring an interpolation between the measurement points inside the wearable insole sensor, and two experiments are conducted for performance evaluation. For this purpose, the input data used in the experiment are generated in three types (13 FSRs, 8 FSRs, 5 FSRs) according to the regions and numbers of FSRs. First, the estimation results of the CoP trajectory are compared using Gaussian process regression and weighted mean. As a result of each method, the estimation results of the two methods were similar in the case of 13 FSRs data. On the other hand, in the case of the 8 and 5 FSRs data, the weighted mean varies depending on the regions and numbers of FSRs, but the estimation results of Gaussian process regression showed similar results in spite of reducing the regions and numbers. Second, the estimation results of the CoP trajectory based on Gaussian process regression during several gait cycles are analyzed. In five gait cycles, the previous cycle and the current estimation results are compared, and it was confirmed that similar trajectories appeared in all. In this way, the method of estimating the CoP trajectory based on Gaussian process regression showed robust results, and stability was confirmed by yielding similar results in several gait cycles.

• Ocean Science Journal
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• v.40 no.3
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• pp.127-143
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• 2005

The taxonomic sufficiency approach has been proposed as a surrogate for the typical analysis of species-abundance data, especially in conditions involving prominent pollution gradients. Here, we evaluate the use of taxonomic sufficiency with infralittoral macrobenthic data derived from samples taken in a moderate polluted subtropical environment in southeastern Brazil, analysing five taxonomic levels and including two functional levels throughout polychaete feeding guilds and trophic groups. The data were collected seasonally at nine stations and studied for two abundance data series (0.5 and 1.0 mm sieve mesh-size). The results showed a similar ordination pattern between the two sieve mesh-size, but with the 0.5 mm sieve data a different pattern was observed during austral summer. A slight loss of information was detected using genus, family, polychaete species and their feeding guilds as taxonomic/functional units. These results together with those of the cost! benefit ratio, suggested that the family level seemed to be sufficient to detect the impact caused by moderate pollution in this shallow-water, subtropical environment. In additional, through the use of feeding guilds, similar patterns are obtained. Correlation analysis showed that chlorophyll a, total organic matter, zinc, and chromium sediment content were the variables that best explained the biological pattern observed and not always the best correlation coefficient occurring at the species level. The feeding guild approach seems to be useful and generates interpretable results similar to those obtained with the species level of the whole macroinfauna. The results showed an important cost reduction in the sample processing, suggesting that it is possible to adopt a coarser taxonomic level monitoring program even in species-rich communities.

• Journal of Astronomy and Space Sciences
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• v.30 no.4
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• pp.223-230
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• 2013

Sudden enhancements of daytime NmF2 appeared in Anyang ionosonde data during summer seasons in 2006-2007. In order to investigate the causes of this unusual enhancement, we compared Anyang NmF2's with the total electron contents (GPS TECs) observed at Daejeon, and also with ionosonde data at at mid-latitude stations. First, we found no similar increase in Daejeon GPS TEC when the sudden enhancements of Anyang NmF2 occurred. Second, we investigated NmF2's observed at other ionosonde stations that use the same ionosonde model and auto-scaling program as the Anyang ionosonde. We found similar enhancements of NmF2 at these ionosonde stations. Moreover, the analysis of ionograms from Athens and Rome showed that there were sporadic-E layers with high electron density during the enhancements in NmF2. The auto-scaling program (ARTIST 4.5) used seems to recognize sporadic-E layer echoes as a F2 layer trace, resulting in the erroneous critical frequency of F2 layer (foF2). Other versions of the ARTIST scaling program also seem to produce similar erroneous results. Therefore we conclude that the sudden enhancements of NmF2 in Anyang data were due to the misrecognition of sporadic-E echoes as a F-layer by the auto-scaling program. We also noticed that although the scaling program flagged confidence level (C-level) of an ionogram as uncertain when a sporadic-E layer occurs, it still automatically computed erroneous foF2's. Therefore one should check the confidence level before using long term ionosonde data that were produced by an auto-scaling program.

• International Journal of Highway Engineering
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• v.20 no.3
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• pp.39-46
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• 2018

PURPOSES: This study is to estimate nondestructive strength equation based on probability for bridges using field test data. METHODS : In this study, a series of the field inspection and the test have been performed on 297 existing bridges, in order to evaluate the bridges, based on the test results of the in-depth inspection, and the estimated strengths by means of the nondestructive strength equations are analyzed and compared with results of the core specimen strengths. RESULTS : According to results of analyses, In case of standard design compressive strength of concrete is 18MPa, 21MPa, similar reliability of RILEM equation were 0.89~0.90, but in case of standard design compressive strength of concrete is 35MPa, 40MPa were 0.4~0.56. According to standard design compressive strength of concrete is 40MPa, similar reliability of ultrasonic pulse velocity method equation were 0.56. CONCLUSIONS :RILEM equation had high similar reliability than other equation in case of standard design compressive strength of concrete is 18MPa, 21MPa, but had low similar reliability than other equation in case of standard design compressive strength of concrete is 35MPa, 40MPa. and ultrasonic pulse velocity method equation had low similar reliability than other equation in case of standard design compressive strength of concrete is 40MPa.

• Journal of Korea Spatial Information System Society
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• v.10 no.1
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• pp.29-40
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• 2008

With the spread of mobile devices and advances in communication techknowledges, the needs of application which uses the movement patterns of moving objects in history trajectory data of moving objects gets Increasing. Especially, to design public transportation route or road network of the new city, we can use the similar patterns in the trajectories of moving objects that move on the spatial network such as road and railway. In this paper, we propose a spatio-temporal similar trajectory search algorithm for moving objects on road network. For this, we define a spatio-temporal similarity measure based on the real road network distance and propose a grid-based index structure for similar trajectory search. Finally, we analyze the performance of the proposed similar trajectory search algorithm in order to show its efficiency.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.267-271
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• 2000

Traffic analysis during past years used the Poisson distribution or Markov model, assuming an exponential distribution of packet queue arrival. Recent studies, however, have shown aperiodic and burst characteristics of network traffics. Such characteristics of data traffic enable the scalability of network, QoS, optimized design, when we analyze new traffic model having a self-similar characteristic. This paper analyzes the self-similar characteristics of a small-scale mixed traffic in a network simulation, the real network Traffic.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.263-267
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• 2000

Traffic analysis during past years used the Poisson distribution or Markov model, assuming an exponential distribution of packet queue arrival. Recent studies, however, have shown aperiodic and burst characteristics of network traffics Such characteristics of data traffic enable the scalability of network, QoS, optimized design, when we analyze new traffic model having a self-similar characteristic. This paper analyzes the self-similar characteristics of a small-scale mixed traffic in a network simulation, the real WAN delay time, TCP packet size, and the total network usage.

• Journal of information and communication convergence engineering
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• v.2 no.1
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• pp.15-21
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• 2004

One of the most important issues in designing a network and realizing a service is dealing with traffic characteristics. Recent experimental research on LAN, WAN, and VBR traffic properties has highlighted that real traffic specificities can not be displayed because the current models based on the Poisson assumption under estimate the long range dependency of network traffic and self-similar peculiarities. Therefore, a new approach using self-similarity characteristics as a real traffic model was recently developed. In This paper we discusses the definition of self-similarity traffic. Moreover, real traffic was collected and we generated self-similar data traffic like real traffic to background load. On the existing ABR congestion control algorithm transmission throughput with the representative ERICA, EPRCA and NIST switch algorithm show the efficient reaction about the burst traffic.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.221-226
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• 2021

Collaborative filtering based recommender systems recommend user-preferrable items based on rating history and are essential function for the current various commercial purposes. In order to determine items to recommend, prediction of preference score for unrated items is estimated based on similar rating history. Previous studies usually employ two methods individually, i.e., similar user based or similar item based ones. These methods have drawbacks of degrading prediction accuracy in case of sparse user ratings data or when having difficulty with finding similar users or items. This study suggests a new rating prediction method by integrating the two previous methods. The proposed method has the advantage of consulting more similar ratings, thus improving the recommendation quality. The experimental results reveal that our method significantly improve the performance of previous methods, in terms of prediction accuracy, relevance level of recommended items, and that of recommended item ranks with a sparse dataset. With a rather dense dataset, it outperforms the previous methods in terms of prediction accuracy and shows comparable results in other metrics.