• The KIPS Transactions:PartD
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• v.8D no.6
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• pp.843-852
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• 2001

Statistics concerning software errors indicate that more errors are introduced in analysis and design phase than implementation phase. Therefore, it is needed to check whether the design modeling is appropriate for own function and structure. This paper discussed the effective test method for the object-oriented design model, i.e., UML. A new method was proposed for generating test data. This method consists of category partition theory by the representation each element in UML model with OCL (Object Constraint Language). Test data generated in this way can be used for testing the source code functionality as well as for checking the design model.

• Journal of Internet Computing and Services
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• v.5 no.6
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• pp.1-10
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• 2004

A web robot is a software that has abilities of tracking and collecting web documents on the Internet(l), The performance scalability of recent web robots reached the limit CIS the number of web documents on the internet has increased sharply as the rapid growth of the Internet continues, Accordingly, it is strongly demanded to study on the performance scalability in searching and collecting documents on the web. 'Design of web robot based on Multi-Agent to speed up documents collection ' rather than 'Sequentially executing Web Robot based on the existing Fork-Join method' and the results of analysis on its performance scalability is presented in the thesis, For collection speedup, a Multi-Agent based web robot performs the independent process for inactive URL ('Dead-links' URL), which is caused by overloaded web documents, temporary network or web-server disturbance, after dividing them into each agent. The agents consist of four component; Loader, Extractor, Active URL Scanner and inactive URL Scanner. The thesis models a Multi-Agent based web robot based on 'Amdahl's Law' to speed up documents collection, introduces a numerical formula for collection speedup, and verifies its performance improvement by comparing data from the formula with data from experiments based on the formula. Moreover, 'Dynamic URL Partition algorithm' is introduced and realized to minimize the workload of the web server by maximizing a interval of the web server which can be a collection target.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1011-1020
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• 2004

A lot of work has been done to improve the I/O performance of such a system that store and manage a massive amount of data by distributing them across multiple disks and access them in parallel. Most of the previous work has focused on an efficient mapping from a grid ceil, which is determined bY the interval number of each dimension, to a disk number on the assumption that each dimension is split into disjoint intervals such that entire data space is GRID-like partitioned. However, they have ignored the effects of a GRID partitioning scheme on declustering performance. In this paper, we enhance the performance of mapping function based declustering algorithms by applying a good GRID par-titioning method. For this, we propose an estimation model to count the number of grid cells intersected by a range query and apply a GRID partitioning scheme which minimizes query result size among the possible schemes. While it is common to do binary partition for high-dimensional data, we choose less number of dimensions than needed for binary partition and split several times along that dimensions so that we can reduce the number of grid cells touched by a query. Several experimental results show that the proposed estimation model gives accuracy within 0.5% error ratio regardless of query size and dimension. We can also improve the performance of declustering algorithm based on mapping function, called Kronecker Sequence, which has been known to be the best among the mapping functions for high-dimensional data, up to 23 times by applying an efficient GRID partitioning scheme.

• Proceedings of the PSK Conference
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• 2003.04a
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• pp.255.3-256
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• 2003

Marine Algae of order Chordariales are rich resources of bioactive metabolites. Methanolic extracts of the brown alga /shige Okamurae exhibited potent antioxidative and butyrylcholinesterase(BChE) inhibitory effects. Bio-guided purification [solvent partition, ODS flash, silica flash, gel-filtration on Sephadex LH 20, ODS HPLC] of them gave a compound 1. Its structure was elucidated by detailed analysis of spectroscopic data of 1 and comparison of literature data. A variety of bioassay for 1 is in progress.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.543-553
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• 2002

Principal component analysis is applied to reduce p-dimensions into q-dimensions ( $q {\leq} p$). Any partition of a collection of data points with p and q variables generated by the application of six hierarchical clustering methods is re-classified by discriminant analysis. From the application of discriminant analysis through each hierarchical clustering method, correct classification ratios are obtained. The results illustrate which method is more reasonable in exploratory data analysis.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.67-75
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• 2018

Multidimensional scaling (MDS) is an exploratory analysis of multivariate data to represent the dissimilarity among objects in the geometric low-dimensional space. However, a general MDS map only shows the information of objects without any information about variables. In this study, we used MDS based on the algorithm of Torgerson (Theory and Methods of Scaling, Wiley, 1958) to visualize some clusters of objects in categorical data. For this, we convert given data into a multiple indicator matrix. Additionally, we added the information of levels for each categorical variable on the MDS map by applying the partition method of Shin et al. (Korean Journal of Applied Statistics, 28, 1171-1180, 2015). Therefore, we can find information on the similarity among objects as well as find associations among categorical variables using the proposed MDS map.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.11
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• pp.1525-1528
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• 2011

The purpose of this study was to detect SNPs that were responsible for a carcass trait in Hanwoo populations. A non-parametric model applying a restricted partition method (RPM) was used, which exploited a partitioning algorithm considering statistical criteria for multiple comparison testing. Phenotypic and genotypic data were obtained from the Hanwoo Improvement Center, National Agricultural Cooperation Federation, Korea, in which the pedigree structure comprised 229 steers from 16 paternal half-sib proven sires that were born in Namwon or Daegwanryong livestock testing station between spring of 2002 and fall of 2003. A carcass trait, longissimus dorsi muscle area for each steer was measured after slaughter at approximately 722 days. Three SNPs (19_1, 18_4 and 28_2) near the microsatellite marker ILSTS035 on BTA6, around which the quantitative trait loci (QTL) for meat quality were previously detected, were used in this study. The RPM analyses resulted in two significant interaction effects between SNPs (19_1 and 18_4) and (19_1 and 28_2) at ${\alpha}$ = 0.05 level. However, under a general linear (parametric) model no interaction effect between any pair of the three SNPs was detected, while only one main effect for SNP19_1 was found for the trait. Also, under another non-parametric model using a multifactor dimensionality reduction (MDR) method, only one interaction effect of the two SNPs (19_1 and 28_2) explained the trait significantly better than the parametric model with the main effect of SNP19_1. Our results suggest that RPM is a good alternative to model choices that can find associations of the interaction effects of multiple SNPs for quantitative traits in livestock species.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.97-104
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• 2002

In this paper we describe the method which optimizes the partition of the input space by means of measure of fuzziness for fuzzy neural network. It covers its generation of fuzzy rules for input sub space. It verifies the performance of the system depended on the various time interval of the input. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rule base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. According to the input interval the proposed inference procedure proves that the fast convergence of root mean square error (RMSE) owes to the optimal partition of the input space

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.7
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• pp.1108-1114
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• 2007

Twenty eight non-lactating and non-pregnant adult Serra da Estrela ewes, ranging in body condition score (BCS) from 1 to 4 were used to study the relationships between BCS, live weight (LW), body composition and fat partition. Ewes were slaughtered and their kidney knob and channel fat (KKCF), sternal fat (STF) and omental plus mesenteric fat (OMF) were separated and weighed. Left sides of carcasses as well as the respective lumbar joints were then dissected into muscle, bone and subcutaneous (SCF) and intermuscular fat (IMF). The relationship between LW and BCS was studied using data from 1,396 observations on 63 ewes from the same flock and it was found to be linear. Regression analysis was also used to describe the relationships among BCS and/or LW and weights (kg) and percentages in empty body weight (EBW) of dissected tissues. The prediction of weights and percentages in EBW of total fat (TF) and of all fat depots afforded by BCS was better than that provided by LW. Only the weight of muscle and the percentage of bone in the EBW were more efficiently predicted by LW than by BCS. IMF represented the largest fat depot with a BCS of 1 and 2, whereas SCF was the most important site of fat deposition with a BCS of 3 and 4. Allometric coefficients for each fat depot in TF suggest that the fat deposition order in ewes from this breed is: IMF, OMF, SCF and KKCF. Results demonstrate that BCS is a better predictor than LW of body reserves in this breed and that LJ is a suitable anatomical region to evaluate BCS.

• Advances in environmental research
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• v.7 no.2
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• pp.139-159
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• 2018

Green Leaf Volatiles (GLVs) is a class of biogenically emitted oxygenated hydrocarbons that have been identified as a potential source of Secondary Organic Aerosols (SOA) via aqueous oxidation. The physico-chemical properties of GLVs are vital to understanding their fate and transport in the atmosphere via fog processing, but few experimental data are available. We studied the aqueous solubility, 1-octanol/water partition coefficient, and Henry's law constant ($K_H$) of five GLVs at $25^{\circ}C$: methyl jasmonate, methyl salicylate, 2-methyl-3-buten-2-ol, cis-3-hexen-1-ol, and cis-3-hexenyl acetate. Henry's law constant was also measured at temperatures and ionic strengths typical of fog. Experimental values are compared to scarcely-available literature values, as well as estimations using group and bond contribution methods, property-specific correlations and molecular dynamics simulations. From these values, the partition coefficients to the air-water interface were also calculated. The large Henry's law constant of methyl jasmonate ($8091{\pm}1121M{\cdot}atm^{-1}$) made it the most significant GLV for aqueous phase photochemistry. The HENRYWIN program's bond contribution method from the Estimation Programs Interface Suite (EPI Suite) produced the best estimate of the Henry's constant for GLVs. Estimations of 1-octanol/water partition coefficient and solubility are best when correlating an experimental value of one to find the other. Finally, the scavenging efficiency was calculated for each GLV indicating aqueous phase processing will be most important for methyl jasmonate.