• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.343-345
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• 2004

We propose a new category of fuzzy set-based fuzzy inference systems based on data granulation related to fuzzy space division for each variables. Data granules are viewed as linked collections of objects(data, in particular) drawn together by the criteria of proximity, similarity, or functionality. Granulation of data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.6
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• pp.653-665
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• 2007

Several numerical experiments were carried out to clarify the influence of satellite data assimilation with various spatial resolution on mesoscale meteorological wind and temperature field. Satellite data used in this study is QuikSCAT launched on ADEOS II. QuikSCAT data is reasonable and faithful sea wind data, which have been verified through many observational studies. And numerical model in the study is MM5 developed by NCAR. Difference of wind pattern with and without satellite data assimilation appeared clearly, especially wind speed dramatically reduced on East Sea, when satellite data assimilation worked. And sea breeze is stronger in numerical experiments with RDAPS and satellite data assimilation than that with CDAS and data assimilation. This caused the lower estimated surface temperature in CDAS used cases. Therefore the influence of satellite data assimilation acts differently according to initial data quality. And it is necessary to make attention careful to handle the initial data for numerical simulations.

• Journal of the Society of Naval Architects of Korea
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• v.43 no.3 s.147
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• pp.362-374
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• 2006

Currently, all design information of a hull structure is being first defined on 2D drawings not 3D CAD model at the initial ship design stage and then transferred to following design stages through the 2D drawings. This is caused by the past design practice, limitation on time, and lack of hull structural CAD systems supporting the initial design stage. As a result, the following design tasks such as the process planning and scheduling are being manually performed using the 2D drawings. For solving this problem, a data structure supporting the initial design stage is proposed and a prototype system is developed based on the data structure. The applicability of the system is demonstrated by applying it to various examples. The results show that the system can be effectively used for generating the 3D CAD model of the hull structure at the initial design stage.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.3
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• pp.93-105
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• 2008

Semi-supervised learning uses a small amount of labeled data to predict labels of unlabeled data as well as to improve clustering performance, whereas unsupervised learning analyzes only unlabeled data for clustering purpose. We propose a new clustering-based semi-supervised learning method by reflecting the initial predicted labels of unlabeled data on the objective function. The initial prediction should be done in terms of a discrete probability distribution through a classification method using labeled data. As a result, clusters are formed and labels of unlabeled data are predicted according to the Information of labeled data in the same cluster. We evaluate and compare the performance of the proposed method in terms of classification errors through numerical experiments with blinded labeled data.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.481-488
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• 2007

The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. The k-modes algorithm is to extend the k-means paradigm to categorical domains. The algorithm requires a pre-setting or random selection of initial points (modes) of the clusters. This paper improved the problem of k-modes algorithm, using the Max-Min method that is a kind of methods to decide initial values in k-means algorithm. we introduce new similarity measures to deal with using the categorical data for clustering. We show that the mushroom data sets and soybean data sets tested with the proposed algorithm has shown a good performance for the two aspects(accuracy, run time).

• Journal of Digital Convergence
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• v.16 no.12
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• pp.217-229
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• 2018

This paper aims at applying the data mining such as decision tree, neural network, and logistic regression to an unsold apartment complex in Wirye new town and developing the model forecasting the result of initial sale contract by house unit. Raw data are divided into training data and test data. The order of predictability in training data is neural network, decision tree, and logistic regression. On the contrary, the results of test data show that logistic regression is the best model. This means that logistic regression has more data adaptability than neural network which is developed as the model optimized for training data. Determinants of initial sale are the location of floor, direction, the location of unit, the proximity of electricity and generator room, subscriber's residential region and the type of subscription. This suggests that using two models together is more effective in exploring determinants of initial sales. This paper contributes to the development of convergence field by expanding the scope of data mining.

• Journal of Microbiology and Biotechnology
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• v.13 no.3
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• pp.332-337
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• 2003

Enzyme kinetics data play a vital role in the design of reactors and control of processes. In the present study, kinetic studies on pectinases were carried out. Partially purified polymethylgalacturonase (PMG) and polygalacturonase (PG) were the two pectinases studied. The plot of initial rate vs. initial substrate concentration did not follow the conventional Michaelis-Menten kinetics, but substrate inhibition was observed. For PMG, maximum rate was attained at an initial pectin concentration of 3 g/l, whereas maximum rate was attained when the initial substrate concentration of 2.5 g/l of polygalacturonic acid for PG I and PG II. The kinetic data were fitted to five different kinetic models to explain the substrate inhibition effect. Among the five models tested, the combined mechanism of protective diffusion limitation of both high and inhibitory substrate concentrations (semi-empirical model) explained the inhibition data with 96-99% confidence interval.

• 대한공업교육학회지
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• v.32 no.2
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• pp.199-216
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• 2007

Stream Control Transmission Protocol(SCTP) is a transport layer protocol to support the data transmission. SCTP is similar to Transmission Control Protocol(TCP) in a variety of aspects. However, several features of SCTP including multi-homing and multi-streaming incur the performance difference from TCP. This paper highlights the data transfer during the initial slow start phase in SCTP congestion control composed of slow start phase and congestion avoidance phase. In order to compare the mean transfer time between SCTP and TCP, we experiment with different performance parameters including bandwidth, round trip time, and data length. By varying data length, we also measure the corresponding initial window size, which is one of factors affecting the mean transfer time. For the experiment, we have written server and client applications by C language using SCTP socket API and have measured the transfer time by ethereal program. We transferred data between client and server using round-robin method. Analysis of these experimental results from the testbed implementation shows that larger initial window size of SCTP than that of TCP brings the reduction in the mean transfer time of SCTP compared with TCP by 15 % on average during the initial slow start phase.

• Journal of The Korean Association For Science Education
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• v.20 no.2
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• pp.288-296
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• 2000

In this study, the types and the characteristics of students' responses to anomalous data were investigated. The criteria for classifying students' responses were 'acceptance of validity of anomalous data', 'acceptance of inconsistency between anomalous data and initial theory', and 'change of belief in initial theory'. Seven types of responses were identified as follows: Rejection, reinterpretation, exclusion, uncertainty, peripheral theory change, partial belief change, and theory change. Absolute belief in the intial theory and doubts about methodological accuracy were found to be the major reasons for rejecting anomalous data. The students did not accept the inconsistency between anomalous data and initial theory because they ignored the experimental procedures and focused on the similarity of the experimental results.

• Journal of Information Science Theory and Practice
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• v.2 no.2
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• pp.6-15
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• 2014

Initial based disambiguation of author names is a common data pre-processing step in bibliometrics. It is widely accepted that this procedure can introduce errors into network data and any subsequent analytical results. What is not sufficiently understood is the precise impact of this step on the data and findings. We present an empirical answer to this question by comparing the impact of two commonly used initial based disambiguation methods against a reasonable proxy for ground truth data. We use DBLP, a database covering major journals and conferences in computer science and information science, as a source. We find that initial based disambiguation induces strong distortions in network metrics on the graph and node level: Authors become embedded in ties for which there is no empirical support, thus increasing their sphere of influence and diversity of involvement. Consequently, networks generated with initial-based disambiguation are more coherent and interconnected than the actual underlying networks, and individual authors appear to be more productive and more strongly embedded than they actually are.