• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.402-410
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• 2017

For many years, researchers in face description area have been representing and recognizing faces based on different methods that include subspace discriminant analysis, statistical learning and non-statistics based approach etc. But still automatic face recognition remains an interesting but challenging problem. This paper presents a novel and efficient face image representation method based on Optimized Local Ternary Pattern (OLTP) texture features. The face image is divided into several regions from which the OLTP texture feature distributions are extracted and concatenated into a feature vector that can act as face descriptor. The recognition is performed using nearest neighbor classification method with Chi-square distance as a similarity measure. Extensive experimental results on Yale B, ORL and AR face databases show that OLTP consistently performs much better than other well recognized texture models for face recognition.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1560-1563
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• 2010

The goal of this research is to apply the neural networks model for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks model consists of support vector machine neural networks model (SVM-NNM). The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks model, it is composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of SVM-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Communications of Mathematical Education
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• v.16
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• pp.139-147
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• 2003

While the statistical concept 'order statistics' has a great number of applications in our society ranging from industry to military analysis, it is not necessarily an easy concept to understand for many people. Adding some interesting simulation activities of this concept to the probability or statistics curriculum, however, can enhance the learning curve greatly. A hands-on and a graphic calculator based activities of a missile simulation were introduced by Kim(2003) in the context of order statistics. This article revisits the two activities in his paper and point out a dilemma that occurs from the violation of an assumption on two deviation parameters associated with the missile simulation. A third activity is introduced to resolve the dilemma in the terms of a kernel density estimation which is a nonparametric approach.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.98-105
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• 1999

As the IC(Integrated Circuit) has been densified and complicated, it is required to thorough process control to improve yield. Experts, for this purpose, focused on the process analysis automation, which is came from the strict data management in semiconductor manufacturing. In this paper, we presents the process analysis system that can analyze causes, for a output after processes. Also, the plasma etching process that highly affects yield among semiconductor process is modeled to predict a output before the process. To approach this problem, we use adaptively trained neural networks that exhibit superior accuracy over statistical techniques. And in comparison with methods in other paper, a method that history of trend for input data is considered is shown to offer advantage in both learning and prediction capability. This research regards CD(Critical Dimension) that is considerable in high integrated circuit as output variable of the prediction model.

• ETRI Journal
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• v.41 no.3
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• pp.298-307
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• 2019

This paper investigates the use of the inverse-free sparse Bayesian learning (SBL) approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM)-based multiuser massive multiple-input multiple-output (MIMO) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought-after low-PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation-maximization (EM) iterative algorithm. The matrix inversion involved in the expectation step (E-step) is averted by invoking a relaxed evidence lower bound (relaxed-ELBO). The resulting inverse-free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations.

• Journal of Christian Education in Korea
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• v.69
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• pp.139-162
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• 2022

This study verified the effect of learning motivation on immersion by non-face-to-face class method. For this purpose, 101 college students majoring in early childhood education were selected as research subjects. The average age of the study subjects was 22.6 years old, and 51 students took non-real-time non-face-to-face classes, and 50 students took real-time non-face-to-face classes. The study measured the level of immersion and the type of learning motivation after the non-face-to-face class was finished. The measured data were analyzed using descriptive statistical analysis and multiple regression analysis. As a result, in the results for all students, the performance approach goal had the most influence on immersion, and the mastery goal orientation had the next effect. Performance avoidance orientation had no effect. For students in non-face-to-face classes, performance approach goal orientation had an effect on immersion, and for students in real-time non-face-to-face classes, mastery goal orientation had an effect. The implications that can be obtained from the results of this study are as follows. First, non-real-time non-face-to-face classes should cover basic knowledge and skills so that there are no mistakes and failures. Second, non-real-time non-face-to-face classes should allow tasks with appropriate difficulty to be performed with a deadline. Third, real-time non-face-to-face classes should lower the fear of mistakes and failures.

• Journal of the Korean Society for information Management
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• v.18 no.3
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• pp.265-284
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• 2001

The aim of this study was to discover the correlation between users cognitive style and their attitude towards evaluating the system. Postgraduate students cognitive styles were defined as Verbaliser/Imager and Wholist/Analytic, and the functionality and ease of learning features of a Web-based OPAC(Online Public Access Catalogue) system were evaluated using a combined evaluation methods: interviews for the preliminary survey, a questionnaire far the central data collection, and a psychometric approach for the judgement of students cognitive style using Ridings CSA(Cognitive Style Assessment) tool. Forty-four postgraduate student volunteers responded and data was analysed using SPSS(Statistical Package for Social Science) for Windows. The statistical analysis of each feature of the evaluation, the correlation between the variables, and the features were explored using Pearsons correlation coefficients(r). In exploring the effects of the cognitive styles of individuals, this study has failed to reveal a significant (P < 0.05) correlations in the interactive Web-based OPACs evaluation. It could be said that the contribution of cognitive styles to evaluating Web-based OPACs is likely to be weaker than that of non-cognitive (or demographic) variables.

• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.3
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• pp.39-47
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• 2012

General concern over 'Quality of Life(QOL)' has caused many researches, which compare nations' or cities' QOL by the normative criteria proposed by themselves. The fact that these are characterized by subjectiveness makes this study have a purpose of trying to enhance the intersubjectiveness by means of quantitive analysis to find the factors on the QOL. This study uses statistical methods such as multiple regression and factor analysis based on the secondary data from the "2011 Seoul Survey". The survey includes many items, for example happiness index and satisfaction for work, amenity, etc.. And the analysis tells three findings as follows; Firstly, five subcategories of happiness have relative importance in the order of (1)financial condition, (2)health, (3)social activities, (4)community relationship and (5)family life. These generally constitute the first factor extracted by factor analysis and named 'abundance-family-intimacy factor.' Secondly, the 'abundance-family-intimacy factor' and the 'information-danger factor' among five factors(the others are 'learning-giving factor', 'local patriotism-hope for rise factor' and 'amenity-comfort factor') have statistically significant effect to QOL. Thirdly, the first factor has positive effect, but the second has negative to QOL. Note is needed to the fact that the items on SNS and internet belong to second factor and to the result that these make QOL deteriorate. These results should be considered as having limited meaning of statistical aspect. But accumulation of following studies by quantitive approach is anticipate to make more practical and general meaning.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.48-55
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• 2020

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1045-1055
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• 2007

Latent topic models are statistical models which automatically captures salient patterns or correlation among features underlying a data collection in a probabilistic way. They are gaining an increased popularity as an effective tool in the application of automatic semantic feature extraction from text corpus, multimedia data analysis including image data, and bioinformatics. Among the important issues for the effectiveness in the application of latent topic models to the massive data set is the efficient learning of the model. The paper proposes an accelerated learning technique for PLSA model, one of the popular latent topic models, by an incremental EM algorithm instead of conventional EM algorithm. The incremental EM algorithm can be characterized by the employment of a series of partial E-steps that are performed on the corresponding subsets of the entire data collection, unlike in the conventional EM algorithm where one batch E-step is done for the whole data set. By the replacement of a single batch E-M step with a series of partial E-steps and M-steps, the inference result for the previous data subset can be directly reflected to the next inference process, which can enhance the learning speed for the entire data set. The algorithm is advantageous also in that it is guaranteed to converge to a local maximum solution and can be easily implemented just with slight modification of the existing algorithm based on the conventional EM. We present the basic application of the incremental EM algorithm to the learning of PLSA and empirically evaluate the acceleration performance with several possible data partitioning methods for the practical application. The experimental results on a real-world news data set show that the proposed approach can accomplish a meaningful enhancement of the convergence rate in the learning of latent topic model. Additionally, we present an interesting result which supports a possible synergistic effect of the combination of incremental EM algorithm with parallel computing.