• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.13-18
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• 2019

This paper proposes efficient and secure two-way authentication protocol for binding update messages between mobile devices and home agents / correspondent nodes in IoT and Mobile IPv6 (MIPv6) environments with limited computing power and resources. Based on the MIPv6 message exchange, the proposed protocol satisfies both the authentication and the public key exchange optimized for both sides of the communication with minimum modification. In the future, we will carry out a performance analysis study by implementing the proposed protocol in detail.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.701-714
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• 2020

Quantitative high throughput screening (qHTS) assays are used to assess toxicity for many chemicals in a short period by collectively analyzing them at several concentrations. Data are routinely analyzed using nonlinear regression models; however, we propose a new method to analyze qHTS data using a nonlinear mixed effects model. qHTS data are generated by repeating the same experiment several times for each chemical; therefor, they can be viewed as if they are repeated measures data and hence analyzed using a nonlinear mixed effects model which accounts for both intra- and inter-individual variabilities. Furthermore, we apply a one-step approach incorporating robust estimation methods to estimate fixed effect parameters and the variance-covariance structure since outliers or influential observations are not uncommon in qHTS data. The toxicity of chemicals from a qHTS assay is classified based on the significance of a parameter related to the efficacy of the chemicals using the proposed method. We evaluate the performance of the proposed method in terms of power and false discovery rate using simulation studies comparing with one existing method. The proposed method is illustrated using a dataset obtained from the National Toxicology Program.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.141-152
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• 2021

A new statistical model needs additional variables in order to re-evaluate the undecided inference. Then the MNAR assumption is required, since the probabilities for the positivity of the indeterminant and the determinant is calculated differently. In this study, since two statistical models have a hierarchical relationship, we determine the undecided inference under the MNAR assumption using the confidence interval of the difference between two AUCs. Among many methods of estimating the confidence interval of the AUC difference, it is found that four kinds of methods show excellent performance through simulations. And based on these methods, we propose a variable selection method that are useful for the undecided inference using logistic regression models.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.177-191
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• 2022

This paper addresses the use of machine learning methods for causal estimation of treatment effects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure effects, for example, in the research of comparative effectiveness and safety of treatments, where the causal effect can be identified if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more efficient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the final estimate of the treatment effect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their finite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.

• Journal of KIISE
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• v.45 no.2
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• pp.99-105
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• 2018

One of the spatial statistical analysis, hotspot analysis is one of easy method of see spatial patterns. It is based on the concept that "Adjacent ones are more relevant than those that are far away". However, in hotspot analysis is spatial adjacency must be considered, Therefore, distributed processing is not easy. In this paper, we proposed a distributed algorithm design for hotspot spatial analysis. Its performance was compared to standalone system and Hadoop, Spark based processing. As a result, it is compare to standalone system, Performance improvement rate of Hadoop at 625.89% and Spark at 870.14%. Furthermore, performance improvement rate is high at Spark processing than Hadoop at as more large data set.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1684-1699
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• 2017

The deblocking filter (DF) reduces blocking artifacts in encoded video sequences, and thereby significantly improves the subjective and objective quality of videos. Statistics show that the DF accounts for 5-18% of the total decoding time in high-efficiency video coding. Therefore, speeding up the DF will improve codec performance, especially for the decoder. In view of the rapid development of multicore technology, we propose a parallel DF scheme based on a modified order of accessing the coding tree units (CTUs) by analyzing the data dependencies between adjacent CTUs. This enables the DF to run in parallel, providing accelerated performance and more flexibility in the degree of parallelism, as well as finer parallel granularity. We additionally solve the problems of variable privatization and thread synchronization in the parallelization of the DF. Finally, the DF module is parallelized based on the HM16.1 reference software using OpenMP technology. The acceleration performance is experimentally tested under various numbers of cores, and the results show that the proposed scheme is very effective at speeding up the DF.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1169-1178
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• 2010

This article consider a performance model selection based on AIC under unbalanced deign in linear mixed effect models. Vaida and Balanchard (2005) proposed conditional AIC for model selection in linear mixed effect models when the prediction of random effects is of primary interest. Theoretical properties of cAIC and related criteria have been investigated by Liang et al. (2008) and Greven and Kneib (2010). However, all of the simulation studies were performed under a balanced design. Even though functional form of AIC remain same even under the unbalanced deign, it is worthwhile to investigate performance of AIC based model selection criteria under the unbalanced design. The simulation study in this article shows how unbalancedness affects model selection in linear mixed effect models.

• Spatial Information Research
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• v.18 no.3
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• pp.73-83
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• 2010

This study presents a method to segment an image, employing the statistics observed at each pixel location across sequential frame images. In the acquisition and analysis of spatial information, utilization of digital image processing technique has very important implications. Various image segmentation techniques have been presented to distinguish the area of digital images. In this study, based on the analysis of the spectroscopic characteristics of sequential frame images that had been previously researched, an image segmentation method was proposed by using the randomness occurring among a sequence of frame images for a same scene. First of all, we computed the mean and standard deviation values at each pixel and found reliable pixels to determine seed points using their standard deviation value. For segmenting an image into individual regions, we conducted region growing based on a T-test between reference and candidate sample sets. A comparative analysis was conducted to assure the performance of the proposed method with reference to a previous method. From a set of experimental results, it is confirmed that the proposed method using a sequence of frame images segments a scene better than a method using a single frame image.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.671-681
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• 2019

Studies on predicting arrhythmia using machine learning have been actively conducted with increasing number of arrhythmia patients. Existing studies have predicted arrhythmia based on multivariate data of feature variables extracted from RR interval data at a specific time point. In this study, we consider that the pattern of the heart state changes with time can be important information for the arrhythmia prediction. Therefore, we investigate the usefulness of predicting the arrhythmia with multivariate time series data obtained by extracting and accumulating the multivariate vectors of the feature variables at various time points. When considering 1-nearest neighbor classification method and its ensemble for comparison, it is confirmed that the multivariate time series data based method can have better classification performance than the multivariate data based method if we select an appropriate time series distance function.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.901-916
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• 2017

Recently, the industrial paradigm shift to the fourth industry has already begun, and the importance of patents as intangible intellectual property in the fourth industry era is increasing day by day. Since the technical valuation of a patent is calculated according to the opinion of experts, it is costly and time consuming, and hence, the quality of the patent is judged based on subjective opinions of non-experts. Therefore, it is necessary to develop an objective and rational evaluation system for the qualitative level of patents. In this paper, we classify the valuation of patents into technicality, rights, and usability, and consider the quantitative and objective evaluation modeling of patents using Bayesian structural equation model. In particular, based on the data collected by the Korea Invention Promotion Association, we apply the Bayesian approach, which is capable of stable modeling even under small samples by using prior information, and the structural equation model, which is excellent for modeling and evaluating qualitative performance that is difficult to measure directly, to develop a patent evaluation model.