• Title/Summary/Keyword: EM 알고리즘

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A Design and Implementation of Learning System to Support Indoor and Outdoor Field Trips Using RFID Mobile Device (RFID 모바일 기기용 실내.외 체험학습 시스템 설계 및 구현)

  • Yu, Jeong-Su;Baek, Hyeon-Gi
    • Journal of The Korean Association of Information Education
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    • v.14 no.4
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    • pp.527-536
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    • 2010
  • Ubiquitous computing and mobile technologies provide much scope for designing innovative learning experiences that can take place in a variety of indoor and outdoor settings, such as botanical gardens, parks, museums and classrooms. In this paper, we present our own innovative work for bridging indoor and outdoor field-trip learning activities with the support of RFID with reader device on ubiquitous environments. The function of our system is based on strategically located RFID tags placed on objects around settings which are identified using the mobile device installed to an RFID reader. The mobile device reads the RFID tag and sends the learner's request to the field-trip learning system. And learners can be learning activities according to theirs learning levels. We also focus on the use of intelligent agents to customize learning contents for individual learners. The result of our experiment indicates that learner enjoyed learning where mobile devices are used in the system, supporting the learning activities in the context of which they are taking place.

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The Intelligent Control System for Biped Robot Using Hierarchical Mixture of Experts (계층적 모듈라 신경망을 이용한 이동로봇 지능제어기)

  • Choi Woo-Kyung;Ha Sang-Hyung;Kim Seong-Joo;Kim Yong-Taek;Jeon Hong-Tae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.16 no.4
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    • pp.389-395
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    • 2006
  • This paper proposes the controller for biped robot using intelligent control algorithm. In order to simplify the complexity of biped robot control, manipulator of biped robot is divided into four modules. These modules are controlled by intelligent algorithm with Hierarchical Mixture of Experts(HME) using neural network. Also neural network having direct control method learns the inverse dynamics of biped robot. The HME, which is a network of tree structure, reallocates the input domain for the output by learning pattern of input and output. In this paper, as a result of learning HME repeatedly with EM algorithm, the controller for biped robot operating safety walking is designed by modelling dynamics of biped robot and generating virtual error of HME.

Nonignorable Nonresponse Imputation and Rotation Group Bias Estimation on the Rotation Sample Survey (무시할 수 없는 무응답을 가지고 있는 교체표본조사에서의 무응답 대체와 교체그룹 편향 추정)

  • Choi, Bo-Seung;Kim, Dae-Young;Kim, Kee-Whan;Park, You-Sung
    • The Korean Journal of Applied Statistics
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    • v.21 no.3
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    • pp.361-375
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    • 2008
  • We propose proper methods to impute the item nonresponse in 4-8-4 rotation sample survey. We consider nonignorable nonresponse mechanism that can happen when survey deals with sensitive question (e.g. income, labor force). We utilize modeling imputation method based on Bayesian approach to avoid a boundary solution problem. We also estimate a interview time bias using imputed data and calculate cell expectation and marginal probability on fixed time after removing estimated bias. We compare the mean squared errors and bias between maximum likelihood method and Bayesian methods using simulation studies.

Reanalysis of 2002 Donation Frequency Data: Corrections and Supplements (2002년 기부횟수 자료의 재분석: 수정 및 보완)

  • Kim, Byung Soo;Lee, Juhyung;Kim, Inyoung;Park, Su-Bum;Park, Tae-Kyu
    • The Korean Journal of Applied Statistics
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    • v.27 no.5
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    • pp.743-753
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    • 2014
  • Kim et al. (2006) and Kim et al. (2009) reported a set of explanatory variables affecting donation frequency when they analyzed nationwide survey data on donations collected in 2002 by Volunteer 21, a nonprofit organization in Korea. The primary purpose of this paper is to correct computational errors found in Kim et al. (2006) and Kim et al. (2009), to rectify major results in the Tables and Figures and to supplement Kim et al. (2009) by providing new results. We add two logistic regressions to the ZIP and a mixture of two Poisson regressions of Kim et al. (2009). Through these two logistic regressions we could detect a set of explanatory variables affecting donation activity (0 or 1) and another set of explanatory variables, in which the volunteer (0, 1) variable is common, discriminating the infrequent donor group from the frequent donor group.

Analysis of Missing Data Using an Empirical Bayesian Method (경험적 베이지안 방법을 이용한 결측자료 연구)

  • Yoon, Yong Hwa;Choi, Boseung
    • The Korean Journal of Applied Statistics
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    • v.27 no.6
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    • pp.1003-1016
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    • 2014
  • Proper missing data imputation is an important procedure to obtain superior results for data analysis based on survey data. This paper deals with both a model based imputation method and model estimation method. We utilized a Bayesian method to solve a boundary solution problem in which we applied a maximum likelihood estimation method. We also deal with a missing mechanism model selection problem using forecasting results and a comparison between model accuracies. We utilized MWPE(modified within precinct error) (Bautista et al., 2007) to measure prediction correctness. We applied proposed ML and Bayesian methods to the Korean presidential election exit poll data of 2012. Based on the analysis, the results under the missing at random mechanism showed superior prediction results than under the missing not at random mechanism.

A Comparison of Bayesian and Maximum Likelihood Estimations in a SUR Tobit Regression Model (SUR 토빗회귀모형에서 베이지안 추정과 최대가능도 추정의 비교)

  • Lee, Seung-Chun;Choi, Byongsu
    • The Korean Journal of Applied Statistics
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    • v.27 no.6
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    • pp.991-1002
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    • 2014
  • Both Bayesian and maximum likelihood methods are efficient for the estimation of regression coefficients of various Tobit regression models (see. e.g. Chib, 1992; Greene, 1990; Lee and Choi, 2013); however, some researchers recognized that the maximum likelihood method tends to underestimate the disturbance variance, which has implications for the estimation of marginal effects and the asymptotic standard error of estimates. The underestimation of the maximum likelihood estimate in a seemingly unrelated Tobit regression model is examined. A Bayesian method based on an objective noninformative prior is shown to provide proper estimates of the disturbance variance as well as other regression parameters

A Study on Shape Variability in Canonical Correlation Biplot with Missing Values (결측값이 있는 정준상관 행렬도의 형상변동 연구)

  • Hong, Hyun-Uk;Choi, Yong-Seok;Shin, Sang-Min;Ka, Chang-Wan
    • The Korean Journal of Applied Statistics
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    • v.23 no.5
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    • pp.955-966
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    • 2010
  • Canonical correlation biplot is a useful biplot for giving a graphical description of the data matrix which consists of the association between two sets of variables, for detecting patterns and displaying results found by more formal methods of analysis. Nevertheless, when some values are missing in data, most biplots are not directly applicable. To solve this problem, we estimate the missing data using the median, mean, EM algorithm and MCMC imputation methods according to missing rates. Even though we estimate the missing values of biplot of incomplete data, we have different shapes of biplots according to the imputation methods and missing rates. Therefore we use a RMS(root mean square) which was proposed by Shin et al. (2007) and PS(procrustes statistic) for measuring and comparing the shape variability between the original biplots and the estimated biplots.

Generalized Linear Mixed Model for Multivariate Multilevel Binomial Data (다변량 다수준 이항자료에 대한 일반화선형혼합모형)

  • Lim, Hwa-Kyung;Song, Seuck-Heun;Song, Ju-Won;Cheon, Soo-Young
    • The Korean Journal of Applied Statistics
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    • v.21 no.6
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    • pp.923-932
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    • 2008
  • We are likely to face complex multivariate data which can be characterized by having a non-trivial correlation structure. For instance, omitted covariates may simultaneously affect more than one count in clustered data; hence, the modeling of the correlation structure is important for the efficiency of the estimator and the computation of correct standard errors, i.e., valid inference. A standard way to insert dependence among counts is to assume that they share some common unobservable variables. For this assumption, we fitted correlated random effect models considering multilevel model. Estimation was carried out by adopting the semiparametric approach through a finite mixture EM algorithm without parametric assumptions upon the random coefficients distribution.

Factored MLLR Adaptation for HMM-Based Speech Synthesis in Naval-IT Fusion Technology (인자화된 최대 공산선형회귀 적응기법을 적용한 해양IT융합기술을 위한 HMM기반 음성합성 시스템)

  • Sung, June Sig;Hong, Doo Hwa;Jeong, Min A;Lee, Yeonwoo;Lee, Seong Ro;Kim, Nam Soo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.38C no.2
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    • pp.213-218
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    • 2013
  • One of the most popular approaches to parameter adaptation in hidden Markov model (HMM) based systems is the maximum likelihood linear regression (MLLR) technique. In our previous study, we proposed factored MLLR (FMLLR) where each MLLR parameter is defined as a function of a control vector. We presented a method to train the FMLLR parameters based on a general framework of the expectation-maximization (EM) algorithm. Using the proposed algorithm, supplementary information which cannot be included in the models is effectively reflected in the adaptation process. In this paper, we apply the FMLLR algorithm to a pitch sequence as well as spectrum parameters. In a series of experiments on artificial generation of expressive speech, we evaluate the performance of the FMLLR technique and also compare with other approaches to parameter adaptation in HMM-based speech synthesis.

High Speed AES Implementation on 64 bits Processors (64-비트 프로세서에서 AES 고속 구현)

  • Jung, Chang-Ho;Park, Il-Hwan
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.18 no.6A
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    • pp.51-61
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    • 2008
  • This paper suggests a new way to implement high speed AES on Intel Core2 processors and AMD Athlon64 processors, which are used all over the world today. First, Core2 Processors of EM64T architecture's memory-access-instruction processing efficiency are lower than calculus-instruction processing efficiency. So, previous AES implementation techniques, which had a high rate of memory-access-instruction, could cause memory-bottleneck. To improve this problem we present the partial round key techniques that reduce the rate of memory-access-instruction. The result in Intel Core2Duo 3.0 Ghz Processors show 185 cycles/block and 2.0 Gbps's throughputs in ECB mode. This is 35 cycles/block faster than bernstein software, which is known for being the fastest way. On the other side, in AMD64 processors of AMD64 architecture, by removing bottlenecks that occur in decoding processing we could improve the speed, with the result that the Athlon64 processor reached 170 cycles/block. The result that we present is the same performance of Matsui's unpublished software.