• The Korean Journal of Applied Statistics
• /
• v.32 no.1
• /
• pp.41-68
• /
• 2019

Multi-view learning considers data from various viewpoints as well as attempts to integrate various information from data. Multi-view learning has been studied recently and has showed superior performance to a model learned from only a single view. With the introduction of deep learning techniques to a multi-view learning approach, it has showed good results in various fields such as image, text, voice, and video. In this study, we introduce how multi-view learning methods solve various problems faced in human behavior recognition, medical areas, information retrieval and facial expression recognition. In addition, we review data integration principles of multi-view learning methods by classifying traditional multi-view learning methods into data integration, classifiers integration, and representation integration. Finally, we examine how CNN, RNN, RBM, Autoencoder, and GAN, which are commonly used among various deep learning methods, are applied to multi-view learning algorithms. We categorize CNN and RNN-based learning methods as supervised learning, and RBM, Autoencoder, and GAN-based learning methods as unsupervised learning.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.8
• /
• pp.123-131
• /
• 2018

The purpose of this study is to investigate the clinical nursing competence and self-efficacy of 4th and 2nd semester nursing college students who completed most of the performance-based nursing education curriculum. It was attempted to develop competency evaluation and competency-based curriculum. The collected data were analyzed using descriptive statistics, t-test, one-way ANOVA, $scheff{\bar{e}}$ test, Pearson's correlation coefficients and Stepwise multiple regression in SPSS WIN 24.0 program. The findings of this study were as follows. 1) The knowledge level of essential basic nursing skills received a score of 88.95. The overall average score of clinical performance was 3.15 out of 5. The mean score of self-efficacy was $4.14{\pm}0.57$ points on 6 points 2) Among the general characteristics of subjects, 'motivation of major selection' and 'satisfaction of practice time' differed in the knowledge of essential basic nursing skills, 'religion' and 'health status' differed in clinical performance ability and 'interpersonal relationship', 'motivation of major selection', 'major satisfaction', 'satisfaction of practice time', 'nursing satisfaction', 'desired working period' and 'average rating' differed in self-efficacy. 3) The self-efficacy showed a significant positive correlation with the clinical nursing competency including the knowledge of essential basic nursing skills and clinical performance ability. The nursing satisfaction, clinical performance ability, the knowledge of essential basic nursing skills, interpersonal relationship and average rating influenced significantly and explained 23.7% of the subjects' self-efficacy.

• Journal of Korean Clinical Nursing Research
• /
• v.22 no.2
• /
• pp.225-237
• /
• 2016

Purpose: The purpose of this study was to identify the effects of characteristics of nurses on knowledge and nursing performance of evidence based hemodialysis nursing practice among hemodialysis unit nurses. Methods: The participants were 180 nurses working in hemodialysis unit for more than 6 months in 23 private and general hospitals in Seoul and Gyeonggido. Data were collected from March 30th to April 15t in 2016 and were analyzed using stepwise regression analysis, descriptive statistics, t-test and ANOVA. Results: Nurses' knowledge on evidence based hemodialysis nursing practice was $15.87{\pm}4.52$ out of 23 points. Type of hospitals working in and adherence to evidence based hemodialysis guidelines were significant factors to knowledge and these two factors explained 30.0%. Nurses' nursing performance on evidence-based hemodialysis nursing practice was 4.52 out of 5 points. The performace level was significantly related to total nurisng career and necessity of hemodialysis nursing education and these two factors explained 8.0%. Conclusion: A development of guideline and continuing education is necessary for improving knowledge and performance of evidence based hemodialysis nursing practice.

• Communications for Statistical Applications and Methods
• /
• v.25 no.4
• /
• pp.373-383
• /
• 2018

The spatial scan statistic is a widely used method to detect spatial clusters. The method imposes a large number of scanning windows with pre-defined shapes and varying sizes on the entire study region. The likelihood ratio test statistic comparing inside versus outside each window is then calculated and the window with the maximum value of test statistic becomes the most likely cluster. The results of cluster detection respond sensitively to the shape and the maximum size of scanning windows. The shape of scanning window has been extensively studied; however, there has been relatively little attention on the maximum scanning window size (MSWS) or maximum reported cluster size (MRCS). The Gini coefficient has recently been proposed by Han et al. (International Journal of Health Geographics, 15, 27, 2016) as a powerful tool to determine the optimal value of MRCS for the Poisson-based spatial scan statistic. In this paper, we apply the Gini coefficient to normal-based spatial scan statistics. Through a simulation study, we evaluate the performance of the proposed method. We illustrate the method using a real data example of female colorectal cancer incidence rates in South Korea for the year 2009.

• Communications for Statistical Applications and Methods
• /
• v.24 no.6
• /
• pp.627-640
• /
• 2017

A common interest in gene expression data analysis is to identify genes that present significant changes in expression levels among biological experimental conditions. In this paper, we develop a Bayesian approach to make a gene-by-gene comparison in the case with a control and more than one treatment experimental condition. The proposed approach is within a Bayesian framework with a Dirichlet process prior. The comparison procedure is based on a model selection procedure developed using the discreteness of the Dirichlet process and its representation via Polya urn scheme. The posterior probabilities for models considered are calculated using a Gibbs sampling algorithm. A numerical simulation study is conducted to understand and compare the performance of the proposed method in relation to usual methods based on analysis of variance (ANOVA) followed by a Tukey test. The comparison among methods is made in terms of a true positive rate and false discovery rate. We find that proposed method outperforms the other methods based on ANOVA followed by a Tukey test. We also apply the methodologies to a publicly available data set on Plasmodium falciparum protein.

• The Korean Journal of Applied Statistics
• /
• v.31 no.6
• /
• pp.811-823
• /
• 2018

Multivariate control chart based on Hotelling's $T^2$ statistic is a powerful tool in statistical process control for identifying an out-of-control process. It is used to monitor multiple process characteristics simultaneously. Detection of the out-of-control signal with the $T^2$ chart indicates mean vector shifts. However, these multivariate signals make it difficult to interpret the cause of the out-of-control signal. In this paper, we review methods of signal interpretation based on the Mason, Young, and Tracy (MYT) decomposition of the $T^2$ statistic. We also provide an example on how to implement it using R software and demonstrate simulation studies for comparing the performance of these methods.

• Genomics & Informatics
• /
• v.20 no.1
• /
• pp.8.1-8.14
• /
• 2022

Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/ environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2006.05a
• /
• pp.51-56
• /
• 2006

Biases embedded in numerical weather precipitation forecasts by the RDAPS model was determined, quantified and corrected. The ultimate objective is to eventually enhance the reliability of reservoir operation by Korean Water Resources Corporation (KOWACO), which is based on precipitation-driven forecasts of stream flow. Statistical post-processing, so called MOS (Model Output Statistics) was applied to RDAPS to improve their performance. The Artificial Neural Nwetwork (ANN) model was applied for 4 cases of 'Probability of Precipitation (PoP) for wet and dry season' and 'Quantitative Precipitation Forecasts (QPF) for wet and dry season'. The reduction on the large systematic bias was especially remarkable. The performance of both networks may be improved by retraining, probably every month. In addition, it is expected that performance of the networks will improve once atmospheric profile data are incorporated in the analysis. The key to the optimal performance of ANN is to have a large data set relevant to the predictand variable. The more complex the process to be modeled by the ANN, the larger the data set needs to be.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.1
• /
• pp.161-174
• /
• 2007

In this paper, we propose a high performance database cluster system called HyperDB to process OLAP queries efficiently. HyperDB is a virtual database system running on top of internet-connected PCs; the PCs are used for their own purpose at ordinary times, but they are able to participate in the database cluster system at non-office hours. We propose fully logical replication technique and optimal parallel intra-query routing technique for extensibility and performance. Experiment for TPC-R benchmark shows significant performance upgrade compared with conventional approaches.

• Journal of the Korea Society for Simulation
• /
• v.12 no.4
• /
• pp.103-111
• /
• 2003

A distributed sewer cluster system is a cost-effective system to provide a service application for clients with reliable, scalable, available, and fault-tolerant features. In order to provide high quality services, it is necessary to evaluate service performances, tune the server system, and analyze performances. In this paper, we propose a simulator to generate workloads based on statistic configuration according to estimated application traffics, apply workload scheduling algorithms, and evaluate the simulation results. We introduce the simulator design modelling and architecture. By using flexible parameters, the simulator is able to generate various patterns of workloads with different statistics, and configure system environments such as the number of server nodes, system resources considered, and their capacities. With this simulator, we introduce two scenarios: one is to find appropriate thresholds for the best performance of cluster system, and the other is to find the suitable scheduling algorithm for workload characteristics of service applications.