• Journal of the Optical Society of Korea
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• v.20 no.2
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• pp.276-282
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• 2016

A 2 Gbit/s visible-light communication (VLC) scheme using time-frequency color-clustered (TFCC) multiple-input multiple-output (MIMO) based on blue, cyan, yellow, and red (BCYR) light-emitting diodes (LEDs) is presented. In the proposed scheme, BCYR LEDs are employed to form four different color clusters. Data transmission using the four color clusters is performed in MIMO, so that the scheme achieves a very high speed of data transmission. Moreover, the scheme employs the TFCC strategy to yield high performance in terms of bit error rate (BER). TFCC operates in such a way that the original data and the two delayed versions of the data are multiplied by orthogonal frequencies and then transmitted using a specific color of the BCYR LED. In the receiver, color filters are employed to detect the data transmitted from the desired cluster. Selection combining (SC) is also performed to yield a diversity effect within each color cluster, to further improve the performance. Performance evaluation demonstrates that the proposed TFCC MIMO VLC offers a data rate of 2 Gbit/s and a bit error rate of 4×10^-5, at an E_b/N_o value of merely 3 dB.

• Journal of the Korean Home Economics Association
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• v.20 no.4
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• pp.83-89
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• 1982

The purpose of this study was to analyze the 45 measuring items for the clothing construction in order to observe the factorial structure of items and to extract the common factor and the special unique factor from data. The sample for the study was drawn randomly out of senior high schoolgirls in Seoul urban area. The size of sample was 301 girls between age 16 and 18. The method of analysis was applied by the principal component analysis with orthogonal rotation after extraction of 9 major factors. All of the above data was analyzed by the computer installed at Seoul National University. From these analyses, the major findings can be summerized as follows: 1. The results of factor analysis generally indicated that the first factor was clustered with 15 items, length measures and height measures. The eigenvalue of the first factor was 16.5 and the cumulative percentage of variables 36.6%. 2. The second factor was clustered with width measures, girth measures and weight of 19 items. The eigenvalue of the second factor was 6.5 and the cumulative percentage of variables 51.0%.

• Journal of the Korea Society of Computer and Information
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• v.23 no.5
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• pp.9-14
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• 2018

For an efficient management of electricity market and power systems, accurate forecasts for electricity demand are essential. Since there are many factors, either known or unknown, determining the realized loads, it is difficult to forecast the demands with the past time series only. In this paper we perform a cluster analysis on electricity demand data collected from Jan. 2000 to Dec. 2017. Our purpose of clustering on electricity demand data is that each cluster is expected to consist of data whose latent variables are same or similar values. Then, if properly clustered, it is possible to develop an accurate forecasting model for each cluster separately. To validate the feasibility of this approach for building better forecasting models, we clustered data with t-SNE. To apply t-SNE to time series data effectively, we adopt the dynamic time warping as a similarity measure. From the result of experiments, we found that several clusters are well observed and each cluster can be interpreted as a mix of well-known factors such as trends, seasonality and holiday effects and other unknown factors. These findings can motivate the approaches which build forecasting models with respect to each cluster independently.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.9
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• pp.715-721
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• 2016

In this paper, the classification rate of micro-cracks in silicon wafers was improved using a SVM. In case I, we investigated how feature data of micro-cracks and SVM parameters affect a classification rate. As a result, weighting vector and bias did not affect the classification rate, which was improved in case of high cost and sigmoid kernel function. Case II was performed using a more high quality image than that in case I. It was identified that learning data and input data had a large effect on the classification rate. Finally, images from cases I and II and another illumination system were used in case III. In spite of different condition images, good classification rates was achieved. Critical points for micro-crack classification improvement are SVM parameters, kernel function, clustered feature data, and experimental conditions. In the future, excellent results could be obtained through SVM parameter tuning and clustered feature data.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.331-343
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• 2014

We propose two estimating procedures to analyze clustered interval-censored data with an informative cluster size based on a marginal model and investigate their asymptotic properties. One is an extension of Cong et al. (2007) to interval-censored data and the other uses the within-cluster resampling method proposed by Hoffman et al. (2001). Simulation results imply that the proposed estimators have a better performance in terms of bias and coverage rate of true value than an estimator with no adjustment of informative cluster size when the cluster size is related with survival time. Finally, they are applied to lymphatic filariasis data adopted from Williamson et al. (2008).

• FLOWER RESEARCH JOURNAL
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• v.19 no.4
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• pp.225-230
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• 2011

This research was performed for making data-base of cross-breeding between Cymbidium goeringii cultivars. Morphological characteristics were investigated and then genetic relationship was analyzed. Collected 20 Cymbidium goeringii cultivars were clustered into 2 groups. Seven cultivars were clustered into group I, and thirteen cultivars were clustered into group II. Group I doesn't have leaf pattern. Group have leaf pattern. The genetic relationship among collected 20 Cymbidium goeringii cultivars was anaylzed using RAPD with ten 10-mers random primer. Eighty-nine bands were generated by RAPD. Among the rest, three bands were monomorphic and eight-six bands were polymorphic. Overall similarity degree ranged from 0.521 to 0.862. The result of RAPD analysis was clustered into 2 groups, too. Sixteen cultivars were clustered into GroupX, and four cultivars were clustered into GroupY. Result of classification with morphological characteristics and RAPD showed different pattern, but 4 cultivars of GroupY by RAPD analysis were included in groupby morphological characteristics. Crossbreeding combination among low related coltivars in RAPD analysis may get more efficient result.

• Korean Journal of Child Studies
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• v.35 no.3
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• pp.71-92
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• 2014

Using the second year data of the Korean Child and Youth Panel Survey (KCYPS) elementary 4 panel, this study attempted to elucidate variables related to elementary school 5th graders' life satisfaction and how the variables are clustered in each gender. Analyzing the data of 2378 5th graders (boys 1180, girls 1084) indicated that variables related to their life satisfaction were self-esteem, parenting style, peer attachment, grade satisfaction, and school adjustment. Both boys and girls were clustered into three clusters. The cluster 1 children indicated the highest degrees of self-esteem, peer attachment, grade satisfaction and school adjustment levels, and they perceived parenting style more positively than the children from the other clusters. The cluster 3 children showed the opposite trends to the cluster 1 children in each of the five variables and the cluster 2 showed middle levels in all of the variables. The characteristics of the three clusters were analyzed in terms of the differences of children's life satisfaction and explanatory variables of life satisfaction.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.93-107
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• 2002

Random effects generalised linear models are useful for analysing clustered count data in which responses are usually correlated. We propose a Bayesian approach to parameter estimation and variable selection in random effects generalised linear models for count data. A simple Gibbs sampling algorithm for parameter estimation is presented and a simple and efficient variable selection is done by using the Gibbs outputs. An illustrative example is provided.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.12-19
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• 2020

The clustered heterogeneous wireless sensor network is comprised of sensor nodes and cluster heads, which are hierarchically organized for different objectives. In the network, we should especially take care of managing node resources to enhance network performance based on memory and battery capacity constraints. For instances, if some interesting events occur frequently in the vicinity of particular sensor nodes, those nodes might receive massive amounts of data. Data congestion can happen due to a memory bottleneck or link disconnection at cluster heads because the remaining memory space is filled with those data. In this paper, we utilize drones as mobile sinks to resolve data congestion and model the network, sensor nodes, and cluster heads. We also design a cost function and a congestion indicator to calculate the degree of congestion. Then we propose a data congestion map index and a data congestion mapping scheme to deploy drones at optimal points. Using control variable, we explore the relationship between the degree of congestion and the number of drones to be deployed, as well as the number of drones that must be below a certain degree of congestion and within communication range. Furthermore, we show that our algorithm outperforms previous work by a minimum of 20% in terms of memory overflow.

• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.566-571
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• 2009

This paper proposes noise-robust fast speaker adaptation method based on the eigenvoice framework in various noisy environments. The proposed method is focused on de-noising and environment clustering. Since the de-noised adaptation DB still has residual noise in itself, environment clustering divides the noisy adaptation data into similar environments by a clustering method using the cepstral mean of non-speech segments as a feature vector. Then each adaptation data in the same cluster is used to build an environment-clustered speaker adapted (SA) model. After selecting multiple environmentally clustered SA models which are similar to test environment, the speaker adaptation based on an appropriate linear combination of clustered SA models is conducted. According to our experiments, we observe that the proposed method provides error rate reduction of $40{\sim}59%$ over baseline with speaker independent model.