• Journal of KIISE
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• v.41 no.12
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• pp.1081-1089
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• 2014

Context-sensitive spelling-error correction methods are largely classified into rule-based methods and statistical data-based methods, the latter of which is often preferred in research. Statistical error correction methods consider context-sensitive spelling error problems as word-sense disambiguation problems. The method divides a vocabulary pair, for correction, which consists of a correction target vocabulary and a replacement candidate vocabulary, according to the context. The present paper proposes a method that integrates a word-phrase n-gram model into a conventional model in order to improve the performance of the probability model by using a correction vocabulary pair, which was a result of a previous study performed by this research team. The integrated model suggested in this paper includes a method used to interpolate the probability of a sentence calculated through each model and a method used to apply the models, when both methods are sequentially applied. Both aforementioned types of integrated models exhibit relatively high accuracy and reproducibility when compared to conventional models or to a model that uses only an n-gram.

• Journal of Applied Reliability
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• v.16 no.3
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• pp.224-230
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• 2016

Purpose: Military maintenance involves corrective and preventive actions carried out to keep a system in or restore it to a predetermined condition. This research develops an optimal maintenance cycle for aviation oil testing equipment with acceptable reliability level and minimum maintenance cost. Methods: The optimal maintenance policy in this research aims to satisfy the desired reliability level at the lowest cost. We assume that the failure process of equipment follows the power law non-homogeneous Poisson process model and the maintenance system is a minimal repair policy. Estimation and other statistical procedures (trend test and goodness of fit test) are given for this model. Results: With time varying failure rate, we developed reliability-based maintenance cost optimization model. This model will reduce the ownership cost through adopting a proactive reliability focused maintenance system. Conclusion: Based on the analysis, it is recommended to increase the current maintenance cycle by three times which is 0.5 year to 1.5 years. Because of the system's built-in self-checking features, it is not expected to have any problems of preventative maintenance cycle.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.35-40
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• 2017

Out-of-limit (OOL) alarm method that is threshold checking of telemetry value is widely used for the satellites fault diagnosis and health monitoring. However, it requires engineering knowledge and effort to define delicate threshold value and has limitations that anomalous behaviors within the defined limits can't be detected. In this paper, we propose a satellite anomalous behavior detection system through fuzzy model that is composed by important statistical feature selected by rough-set theory. Not pre-defined anomaly is detected because only normal state data is used for fuzzy model. Also, anomalous behavior within the threshold limit is detected by using statistic feature that can be collected without engineering knowledge. The proposed system successfully detected non-ordinary state for battery temperature telemetry.

• Journal of Korean Society of Water and Wastewater
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• v.12 no.4
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• pp.43-52
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• 1998

The purpose of this study is to establish a method of estimating the daily urban water demend using Backpropagation algorithm is part of ANN(Artificial Neural Network). This method will be used for the development of the efficient management and operations of the water supply facilities. The data used were the daily urban water demend, the population and weather conditions such as treperarture, precipitation, relative humidity, etc. Kwangju city was selected for the case study area. We adjusted the weights of ANN that are iterated the training data patterns. We normalized the non-stationary time series data [-1,+1] to fast converge, and choose the input patterns by statistical methods. We separated the training and checking patterns form input date patterns. The performance of ANN is compared with multiple-regression method. We discussed the representation ability the model building process and the applicability of ANN approach for the daily water demand. ANN provided the reasonable results for time series forecasting.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.677-693
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• 2011

In this paper, we consider Bayesian approaches to zero inflated Poisson model, one of the popular models to analyze zero inflated count data. To generate posterior samples, we deal with a Markov Chain Monte Carlo method using a Gibbs sampler and an exact sampling method using an Inverse Bayes Formula(IBF). Posterior sampling algorithms using two methods are compared, and a convergence checking for a Gibbs sampler is discussed, in particular using posterior samples from IBF sampling. Based on these sampling methods, a real data analysis is performed for Trajan data (Marin et al., 1993) and our results are compared with existing Trajan data analysis. We also discuss model selection issues for Trajan data between the Poisson model and zero inflated Poisson model using various criteria. In addition, we complement the previous work by Rodrigues (2003) via further data analysis using a hierarchical Bayesian model.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.85-92
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• 2019

Companies can identify user groups or consumption trends by collecting and analyzing opinions of many users on special subjects or their products as well as utilize them as various purposes such as predicting some specific trends or marketing strategies. Therefore current analyzing tools of social media have come into use as a means to measure the performances of social media marketing through network's statistical analysis. However these tools require expensive computing and network resources including burden of costs for building up and operating complex software platforms and much operating know-how. Hence, small companies or private business operators have difficulty in utilizing those social media data effectively. This paper proposes a framework applied into developing analysis system of social media. The framework could be set up and operate the system to extract necessary social media's data. Also to design the system, this study suggests a meta-model of proposed framework and to guarantee completeness and consistency, a formal specification of meta-model by using Z language is suggested. Finally, we could verify the clearness of framework's design by performing Z model checking of formal specification's output through Z-EVES tool.

• Journal of Biosystems Engineering
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• v.39 no.3
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• pp.151-157
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• 2014

Purpose: In order to develop strategies to prevent farm-work accidents relating to agricultural machinery, influential factors were examined in this paper. The effects of these factors were quantified using logistic regression. Methods: Based on the results of a survey on farm-work accidents conducted by the National Academy of Agricultural Science, 21 tentative independent variables were selected. To apply these variables to regression, the presence of multicollinearity was examined by comparing correlation coefficients, checking the statistical significance of the coefficients in a simple linear regression model, and calculating the variance inflation factor. A logistic regression model and determination method of its goodness of fit was defined. Results: Among 21 independent variables, 13 variables were not collinear each other. The results of a logistic regression analysis using these variables showed that the model was significant and acceptable, with deviance of 714.053. Parameter estimation results showed that four variables (age, power tiller ownership, cognizance of the government's safety policy, and consciousness of safety) were significant. The logistic regression model predicted that the former two increased accident odds by 1.027 and 8.506 times, respectively, while the latter two decreased the odds by 0.243 and 0.545 times, respectively. Conclusions: Prevention strategies against factors causing an accident, such as the age of farmers and the use of a power tiller, are necessary. In addition, more efficient trainings to elevate the farmer's consciousness about safety must be provided.

• Journal of Korean Society for Quality Management
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• v.42 no.4
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• pp.747-756
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• 2014

Purpose: The purpose of this study is to apply the Bayesian estimation methodology for producing 'Korean Standard -Quality Excellence Index' model and prove the effectiveness of the new approach based on survey data by comparing the current index with the new index produced by Bayesian estimation method. Methods: The 'Korean Standard -Quality Excellence Index' was produced through the collected survey data by Bayesian estimation method and comparing the deviation with two results for confirming the effectiveness of suggested application. Results: The statistical analysis result shows that suggested estimator, that is, empirical Bayes estimator improves the effectiveness of the index with regard to reduce the error under specific loss function, which is suggested for checking the goodness of fit. Conclusion: Considering the Bayesian techniques such as empirical Bayes estimator for producing the quality excellence index reduces the error for estimating the parameter of interest and furthermore various Bayesian perspective approaches seems to be meaningful for producing the corresponding index.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.18-27
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• 2019

The process control methods based on the statistical analysis apply the analysis method or mathematical model under the assumption that the process characteristic is normally distributed. However, the distribution of data collected by the automatic measurement system in real time is often not followed by normal distribution. As the statistical analysis tools, the process capability index (PCI) has been used a lot as a measure of process capability analysis in the production site. However, PCI has been usually used without checking the normality test for the process data. Even though the normality assumption is violated, if the analysis method under the assumption of the normal distribution is performed, this will be an incorrect result and take a wrong action. When the normality assumption is violated, we can transform the non-normal data into the normal data by using an appropriate normal transformation method. There are various methods of the normal transformation. In this paper, we consider the Box-Cox transformation among them. Hence, the purpose of the study is to expand the analysis method for the multivariate process capability index using Box-Cox transformation. This study proposes the multivariate process capability index to be able to use according to both methodologies whether data is normally distributed or not. Through the computational examples, we compare and discuss the multivariate process capability index between before and after Box-Cox transformation when the process data is not normally distributed.

• Journal of Technologic Dentistry
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• v.39 no.4
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• pp.263-273
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• 2017

Purpose: This study aimed to explore the relations between class distracting factors and class satisfaction of the dental technology students and then provide a primary data to help further related studies and develop educational programs with which instructors can efficiently manage their classroom. Methods: For this study we have conducted a survey started from the beginning of May 2017 to the end of June. The subjects of the survey were Dental Technology students of D-city, K-city, W-city, selected by random sampling method. The questionnaire was self-administrated and 437 valid results were chosen for our analysis among 450 distributed questionnaires. Results: The results of the research was as follows. Firstly, The overall average point of class distracting factors was 2.5 point. The environmental factors were the highest point as 2.59 and as for the subcategories tiredness and drowsiness was the highest point as 2.76. Secondly, The overall average point of class satisfaction turned out 3,88 point and compliance with class and attitude factors gained the highest point as 4.06. Of the subcategories strict roll checking was the highest point as 4.17. Thirdly, As for class distracting factors from general characteristics a statistical significance was shown as follows; 'instructor factor'(p<.01), 'learner factor'(p<.05), 'total class distracting factor'(p<.05) in the area of gender, 'environmental factor'(p<.001), 'total class distracting factor'(p<.01), 'learner factor'(p<.05), 'instructor factor'(p<.05) in the area of gender 'learner factor'(p<.001), 'instructor factor'(p<.001), 'environmental factor'(p<.001), 'total class distracting factor'(p<.01) in the area of class grade, 'environmental factor'(p<.05) in GPA. Fourthly, A statistical significance, a negative correlation (p<.01) were shown between class distracting factors and class satisfaction. Class distracting factor that especially affects the class satisfaction was instructor factor(p<.001) and the explanatory power of the model turned out 14.7%, which was statistically meaningful (p<.001). Conclusion : Results of this study reveal that instructor factor is the key to class satisfaction of the students. So it is crucial that the instructor faithfully prepare for the class to reinforce the students' learning. Additionally further studies should be followed with more subjects and newer perspectives to develop innovative teaching methodology.