• The Journal of Korean Institute of Communications and Information Sciences
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• v.11 no.1
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• pp.73-84
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• 1986

A statistical method is proposed which estimates an edge that is one of the basic features in image understanding. The conventional edge detection techniques are performed well for a deterministic singnal, but are not satisfactory for a statistical signal. In this paper, we use the likelihood function which takes account of the statistical property of a signal, and derive the decision function from it. We propose the maximum likelihood edge detection technique which estimates an edge point which maximizes the decision function mentioned above. We apply this technique to statistecal signals which are generated by using the random number generator. Simnulations show that the statistical edge detection technique gives satisfactory results. This technique is extended to the two-dimensional image and edges are found with a good accuracy.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.955-959
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• 2010

The statistical analysis module is developed for the part of the new standardized geotechnical database computer program. The purpose of this module is that the geotechnical engineers can optimize the underground construction process of the underdeveloped urban area rehabilitation by this module providing the statistical information for the geotechnical decision making and risk assessment. This module will be modified to offer the statistical information sustainable for the newly adapted geotechnical limit-state design methods.

• Journal of Biomedical Engineering Research
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• v.17 no.4
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• pp.433-442
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• 1996

Since one texture property(i.e coarseness, orientation, regularity, granularity) for ultrasound liver ages was not sufficient enough to classify the characteristics of livers, we used multi texture vectors tracted from ultrasound liver images and a statistical classifier. Multi texture vectors are selected among the feature vectors of the normal liver, fat liver and cirrhosis images which have a good separability in those ultrasound liver images. The statistical classifier uses multi texture vectors as input vectors and classifies ultrasound liver images for each multi texture vector by the Bayes decision rule. Then the decision of the liver disease is made by choosing the maximum value from the averages of a posteriori probability for each multi texture vector In our simulation, we obtained higtler correct ratio than that of other methods using single feature vector, for the test set the correct ratio is 94% in the normal liver, 84% in the fat liver and 86% in the cirrhosis liver.

• Restorative Dentistry and Endodontics
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• v.40 no.3
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• pp.249-252
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• 2015

• The Journal of the Society of Korean Medicine Diagnostics
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• v.12 no.2
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• pp.27-40
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• 2008

Background : In spite of the predominant of the theory of Pathogenesis, the method of Pathogenesis classification is depending on the doctor's clinical trials because od the lack of the objective test criteria. Methods and Results : This study is trying to improve the objectiveness of classification using a new statistical method, decision tree. Decision tree method -a classification technique in the statistical analysis- was used to analyze the result of pathogenesis questionnaire instead of using discriminant analysis. As a result, 10 among 38 pathogenesis questionnaire was selected as important questions and 12 terminal nodes was built to classify the pathogenesis. Conclusions : Using only 10 questions shown in the result of decision tree, we can classify and interpret the pathogenesis easily and effectively.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.27-35
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• 2023

This study was a cross-sectional descriptive survey study that confirms the effect on subjects that prefer clinical practice in order to prepare basic data for efficient clinical practice guidance for nursing college students. The study participants were 201 students attending C University, and the data collection period was from October 1 to October 15, 2022. The collected data were analyzed using SPSS 18.0 as descriptive statistics, Pearson correlation, Chi square test, ANOVA test, and Multiple regression test. As a result of the analysis, it was found that clinical decision-making and critical thinking were correlated under the statistical significance level (r=.730, p<0.01). The most favorite clinical practice department was community nursing, and male students preferred community nursing the most (Male=45.6%, χ²=.000), female students were found to prefer similar levels of practical subjects with child nursing , adult nursing, and maternal nursing(χ²=000).Clinical decision-making was found to be higher in students who preferred community nursing at a statistical significance level than those who preferred child nursing (F=4.91, p<0.01). Critical thinking was higher among students who preferred adult nursing than those who preferred other subjects (F=4.65, p<0.01). Through the analysis results, it was found that general characteristics vary, but clinical decision-making ability and critical thinking affect the preference of clinical practice subjects. Therefore, based on the results of this study, the professor of clinical practice suggests the development of a program to foster clinical decision-making and critical thinking to make students interested in clinical practice subjects.

• Asia pacific journal of information systems
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• v.21 no.2
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• pp.19-42
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• 2011

This paper proposes a new framework to predict decision performance by investigating the cognitive fit of decision makers. We assume that every decision maker has two kinds of schema: emotional and rational. Cognitive fit is believed to have a close relationship with the two schemata and decision performance. In the literature on decision performance there is few studies investigating the relationship between the two schemata and cognitive fit. Therefore, our research purposes are twofold: (1) to provide a theoretical basis for the proposed framework describing the causal relationships among the two schemata, cognitive fit. and decision performance, and (2) to empirically prove its validity in the application to an Internet shopping environment. Based on the questionnaires from 104 respondents, we used a second order, confirmatory factor analysis (CFA) model to extract valid constructs, and a structural equation model (SEM) to calculate path coefficients and prove the statistical validity of our proposed research model. The experimental results supported our research model.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.971-980
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• 2003

The tree method can be extended to multivariate responses, such as repeated measure and longitudinal data, by modifying the split function so as to accommodate multiple responses. Recently, some decision trees for multiple responses have been constructed by Segal (1992) and Zhang (1998). Segal suggested a tree can analyze continuous longitudinal response using Mahalanobis distance for within node homogeneity measures and Zhang suggested a tree can analyze multiple binary responses using generalized entropy criterion which is proportional to maximum likelihood of joint distribution of multiple binary responses. In this paper, we will modify CART procedure and suggest a new tree-based method that can analyze multiple binary responses using similarity measures.

• ETRI Journal
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• v.32 no.1
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• pp.22-32
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• 2010

The pervasive game environments have activated explosive growth of the Internet over recent decades. Thus, understanding Internet traffic characteristics and precise classification have become important issues in network management, resource provisioning, and game application development. Naturally, much attention has been given to analyzing and modeling game traffic. Little research, however, has been undertaken on the classification of game traffic. In this paper, we perform an interpretive traffic analysis of popular game applications at the transport layer and propose a new classification method based on a simple decision tree, called an alternative decision tree (ADT), which utilizes the statistical traffic characteristics of game applications. Experimental results show that ADT precisely classifies game traffic from other application traffic types with limited traffic features and a small number of packets, while maintaining low complexity by utilizing a simple decision tree.

• ETRI Journal
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• v.36 no.5
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• pp.714-720
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• 2014

In this paper, we propose the use of data-driven probabilistic utterance-level decision logic to improve Weighted Finite State Transducer (WFST)-based endpoint detection. In general, endpoint detection is dealt with using two cascaded decision processes. The first process is frame-level speech/non-speech classification based on statistical hypothesis testing, and the second process is a heuristic-knowledge-based utterance-level speech boundary decision. To handle these two processes within a unified framework, we propose a WFST-based approach. However, a WFST-based approach has the same limitations as conventional approaches in that the utterance-level decision is based on heuristic knowledge and the decision parameters are tuned sequentially. Therefore, to obtain decision knowledge from a speech corpus and optimize the parameters at the same time, we propose the use of data-driven probabilistic utterance-level decision logic. The proposed method reduces the average detection failure rate by about 14% for various noisy-speech corpora collected for an endpoint detection evaluation.