• Journal of KIISE:Software and Applications
• /
• v.31 no.7
• /
• pp.859-868
• /
• 2004

We propose a method of selecting initial training examples for active learning so that it can reach high accuracy faster with fewer further queries. Our method is based on the assumption that an active learner can reach higher performance when given an initial training set consisting of diverse and typical examples rather than similar and special ones. To obtain a good initial training set, we first cluster examples by using k-means clustering algorithm to find groups of similar examples. Then, a representative example, which is the closest example to the cluster's centroid, is selected from each cluster. After these representative examples are labeled by querying to the user for their categories, they can be used as initial training examples. We also suggest a method of using the centroids as initial training examples by labeling them with categories of corresponding representative examples. Experiments with various text data sets have shown that the active learner starting from the initial training set selected by our method reaches higher accuracy faster than that starting from randomly generated initial training set.

• Journal of the Korean Data Analysis Society
• /
• v.20 no.6
• /
• pp.3049-3067
• /
• 2018

Although there is a growing body of research on organizational commitment profiles based on a person-centered approach, it is not widely applied to the commitment research conducted by Korean organizational scholars yet. Therefore, in this paper, we introduced the concept and analytical methods, such as cluster analysis and latent profile analysis (LPA), of the person-centered approach. In addition, we also performed both cluster analysis and LPA to identify types of organizational commitment profiles of Korean employees based on the combination of affective, continuance and normative commitment on the sample from a range of different fields in South Korea (n = 349). Both analyses extracted two comparable sets of 6 commitment profiles. These six profiles were then contrasted with employee turnover intention. Finally, implications for commitment theory, practices and future research issues were discussed.

• The KIPS Transactions:PartA
• /
• v.14A no.6
• /
• pp.371-382
• /
• 2007

Modern server systems are usually composed in the form of cluster systems in order to serve not only as many users but also as many kinds of applications as possible. The progression of the cluster system architecture leads in a middleware approach based on the Java framework. The middleware approach alleviates the efforts for the construction and the management of a server system but still preserves its performance and applications on the server. In this research, we introduce a new clustering scheme for the easy construction and maintenance of a cluster server system with the Java Management Extensions. We first demonstrate the construction and configuration process. Our experiment sets can verify that it is easy to construct, expand and manage a middleware based cluster system as well as the applications which reside on it. In addition, we can achieve reasonable performance on our service oriented clustered system with the help of state-of-the-art middleware. The experimental results of performance demonstration contain the availability of a server, and the effectiveness of load balancing and scheduling mechanisms. Especially, our service oriented scheduling mechanism was shown to successfully manage load imbalance under the normal load and cope with the overloaded situations, compared with other known scheduling mechanisms.

• The Bulletin of The Korean Astronomical Society
• /
• v.41 no.2
• /
• pp.30.1-30.1
• /
• 2016

We have estimated a metallicity map of the Large Magellanic Cloud (LMC) using the Magellanic Cloud Photometric Survey (MCPS) and Optical Gravitational Lensing Experiment (OGLE III) photometric data. This is a first of its kind, high-spatial resolution map of metallicity up to a radius of $4^{\circ}-5^{\circ}$, derived using large area photometric data and calibrated using spectroscopic data of Red Giant Branch (RGB) stars. The RGB is identified in the V, (V - I) colour- magnitude diagrams of small subregions of varying sizes in both data sets. The slope of the RGB is used as an indicator of the mean metallicity of a subregion, and it is calibrated to metallicity using spectroscopic data for field and cluster red giants in selected subregions. The mean metallicity of the LMC is found to be [Fe/H] = -0.37 dex (${\sigma}[Fe/H]=0.12$) from MCPS data, and [Fe/H] = -0.39 dex (${\sigma}[Fe/H]=0.10$) from OGLE III data. The bar is found to have an uniform and higher metallicity compared to the disk, and is indicative of an active bar in the past. Both the data sets suggest a shallow radial metallicity gradient up to a radius of 4 kpc ($-0.049{\pm}0.002$ dex kpc-1 to $-0.066{\pm}0.006$ dex kpc-1). This metallicity gradient of the LMC disk, though shallow, resembles the gradient seen in spiral galaxies, and similar to that found in our Galaxy.

• Bulletin of the Korean Chemical Society
• /
• v.26 no.1
• /
• pp.119-126
• /
• 2005

The geometrical parameters, vibrational frequencies, and relative energies of H$_2$(A$_{n+1}$X$_{2n}$)H$_2$ (A=C or Si, X=O or S, n = 1-2) oligomers have been investigated using high level ab initio quantum mechanical techniques with large basis sets. The equilibrium geometries have been optimized at the self-consistent field (SCF), the coupled cluster with single and double excitation (CCSD), and the CCSD with connected triple excitations [CCSD(T)] levels of theory. The highest level of theory employed in this study is cc-pVTZ CCSD(T). Harmonic vibrational frequencies and IR intensities are also determined at the SCF level of theory with various basis sets and confirm that all the optimized geometries are true minima. Also zero-point vibrational energies have been considered to predict the dimerization and the relative energies.

• Food Science of Animal Resources
• /
• v.31 no.2
• /
• pp.158-165
• /
• 2011

A sensitive reverse transcriptase-PCR (RT-PCR) method to detect viable Escherichia coli O157:H7 in milk was established. The primer sets were designed based on the nucleotide sequences of the rfbE (per) and wbdN genes in the O157 antigen gene cluster of E. coli O157:H7. RT-PCR using five different primer sets yielded DNA with sizes of 655, 518, 450, and 149-bp, respectively. All five of the E. coli O157:H7 strains were detected by RT-PCR, but 11 other bacterial species were not. The sensitivity of RT-PCR was improved by adding yeast tRNA as a carrier to the crude RNA extract. The RT-PCR amplifying the 149-bp DNA fragment was the most sensitive for detecting E. coli O157:H7 and the most refractory to the bactericidal treatments. Heat treatment at $65^{\circ}C$ for 30 min was the least inhibitory of all bactericidal treatments. Treatment with RNase A strongly inhibited the RT-PCR of heated milk but not unheated milk. This study described RT-PCR methods that are specific and sensitive with a detection limit of 10 E. coli O157:H7 cells, and showed that pre-treating milk samples with RNase A improved the specificity to detect viable bacteria by RT-PCR.

• The Journal of the Korea Contents Association
• /
• v.6 no.9
• /
• pp.9-18
• /
• 2006

We completed a new cluster system based on WDM by proposing a virtual topology reconfiguration schemes. The key idea of the proposed scheme is to construct a set with the longest chains of requests of connecting nodes which need to be assigned a wavelength. All the sets have no common factor, that is, there is no duplicated link among the requests of connecting. After making the set satisfying this condition, we could assign a wavelength to per corresponding set. We could reconfigure a virtual topology with this scheme collectively. we compared our scheme to existing approaches by the OWns simulation tool. As the results, we gained improved performances, reducing 10% of blocking rate and improving 30% of ADM utilization in terms of the blocking probability and the ADM utilization.

• Journal of Korean Society for Atmospheric Environment
• /
• v.13 no.1
• /
• pp.51-63
• /
• 1997

Total suspended particulate matters in the ambient air were analyzed for eight chemical elements (Ca, Co, Cu, Fe, Mn, Pb, Si, and Zn) using an x-ray fluorescence spectrometry (XRF) at the Kyung Hee University - Suwon Campus during 1989 to 1994. To use these data as basis for source identification study, membership of each sample was selected to represent one of the well defined sample groups. The data sets consisting of 83 objects and 8 variables were initially separated into two groups, fine (d$_{p}$<3.3 ${\mu}{\textrm}{m}$) and coarse particle groups (d$_{p}$>3.3 ${\mu}{\textrm}{m}$). A hierarchical clustering method was examined to obtain possible member of homogeneous sample classes for each of the two groups by transforming raw data and by applying various distances. A disjoint principal component analysis was then used to define homogeneous sample classes after deleting outliers. Each of five homogeneous sample classes was determined for the fine and the coarse particle group, respectively. The data were properly classified via an application of logarithmic transformation and Euclidean distance concept. After determining homogeneous classes, correlation coefficients among eight chemical variables within all the homogeneous classes for calculated and meteorological variables (temperature. relative humidity, wind speed, wind direction, and precipitation) were examined as well to intensively interpret environmental factors influencing the characteristics of each class for each group. According to our analysis, we found that each class had its own distinct seasonal pattern that was affected most sensitively by wind direction.ion.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.9
• /
• pp.3029-3036
• /
• 2000

Image segmentation is one of the important processes in the image information extraction for computer vision systems. The fuzzy clustering methods have been extensively used in the image segmentation because it extracts feature information of the region. Most of fuzzy clustering methods have used the Fuzzy C-means(FCM) algorithm. This algorithm can be misclassified about the different size of cluster because the degree of membership depends on highly the distance between data and the centroids of the clusters. This paper proposes a fuzzy clustering algorithm using the Average Intracluster Distance that classifies data uniformly without regard to the size of data sets. The Average Intracluster Distance takes an average of the vector set belong to each cluster and increases in exact proportion to its size and density. The experimental results demonstrate that the proposed approach has the g

• Structural Monitoring and Maintenance
• /
• v.5 no.2
• /
• pp.273-295
• /
• 2018

In this study, an investigation of a damage detection methodology for global condition assessment is presented. A particular emphasis is put on the utilization of wireless sensors for more practical, less time consuming, less expensive and safer monitoring and eventually maintenance purposes. Wireless sensors are deployed with a sensor roving technique to maintain a dense sensor field yet requiring fewer sensors. The time series analysis method called ARX models (Auto-Regressive models with eXogeneous input) for different sensor clusters is implemented for the exploration of artificially induced damage and their locations. The performance of the technique is verified by making use of the data sets acquired from a 4-span bridge-type steel structure in a controlled laboratory environment. In that, the free response vibration data of the structure for a specific sensor cluster is measured by both wired and wireless sensors and the acceleration output of each sensor is used as an input to ARX model to estimate the response of the reference channel of that cluster. Using both data types, the ARX based time series analysis method is shown to be effective for damage detection and localization along with the interpretations and conclusions.