• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.8
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• pp.41-53
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• 1993

The tasks which detect and track moving objects, by analyzing dynamic images taken at a constant time interval, are essential in various applications. This paper suggests how to utilize domain-specific knowledge and motional knowledge for detecting and tracking moving objects. That is, The trajectory information of a moving object is to be used for generating hypotheses on expected motion and expected position of moving objects, and the domain-specific knowledge is to be used for verifying the generated hypotheses.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.671-674
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• 1988

This paper describes a matching method to solve the problem of occlusion in a two dimensional scene. The technique consist of three steps: generation of hypotheses, clustering of hypotheses by matching probability, updating of hypotheses. Using this algorithm, simulation results have been tested for 20 scenes contained the 80 models, and have obtained 95% of properly correct recognition rate in average.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.205-208
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• 2005

We have fuzzy hypotheses testing from Bayesian statistics with ideas from fuzzy sets theory to generalize Bayesian methods both for samples of fuzzy data and for prior distributions with non-precise parameters. Appling the principle of agreement index, the posterior odds ratio in the favor of hypotheses $H_0$ is equal to product of the fuzzy odds ratio and the fuzzy likelihood ratio. If the Posterior odds ratio exceeds the grade judgement, we accept the hypothesis $H_0$ for the degree.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.425-428
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• 2012

We propose some properties for fuzzy hypotheses testing by the principle of the rate of internal division on delta-levels. By the rate of internal division, we show that the acceptance and rejection degree for fuzzy the fuzzy hypotheses and reduce the spread of the fuzzy variance with average of the center and left or right spread of fuzzy number data.

• International Commerce and Information Review
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• v.9 no.1
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• pp.23-41
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• 2007

This paper research the country image, the brand image, consumers' performances and the brand royalty with the path analysis on foreign automobile brands. Our empirical study testify the reliability analysis, the factor analysis and the path analysis with above variables by SPSS 12.0 and AMOS 4.0. The result of this research is that hypotheses H1, H3, H5 were significantly supported, whereas, hypotheses H2, H4 were rejected in the samples of American automobile. and in the samples of Japanese automobile, it was clearly revealed that hypotheses H2, H3, H4 were significantly supported, whereas, hypotheses H1, H5 were rejected. So, in Korean market, we should focus on the brand image strategy more than the country image to make the brand royalty with American automobile products. whereas, Japanese automobile products, another strategy is recommended.

• Annual Conference on Human and Language Technology
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• 2008.10a
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• pp.156-161
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• 2008

This work presents an agenda-based approach to improve the robustness of the dialog manager by using dialog examples and n-best recognition hypotheses. This approach supports n-best hypotheses in the dialog manager and keeps track of the dialog state using a discourse interpretation algorithm with the agenda graph and focus stack. Given the agenda graph and n-best hypotheses, the system can predict the next system actions to maximize multi-level score functions. To evaluate the proposed method, a spoken dialog system for a building guidance robot was developed. Preliminary evaluation shows this approach would be effective to improve the robustness of example-based dialog modeling.

• Journal of the Korean earth science society
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• v.25 no.7
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• pp.586-594
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• 2004

This study is the qualitative study in order to discover a direction of field courses by analyzing what elementary school students observe or hypothesis in field courses. The purpose of it is to find any relevancy between the observations and hypotheses generated. The result of the study were as follows; First, most participants have observed mostly based on their vision due to the characteristic of field courses, and the observations of sedimentary layers were mainly generated on the rocks while most hypotheses were on geological structures. Secondly, according to observational descriptions the frequency of the comparative observation was high as well as the cases when two different types of observations were joined together. The last conclusion from this investigation is, according to the standard of observational types, the hypotheses combined with the interpretive observation and comparative observation had the greatest percentage. This shows that many participants tried to rationalize their thoughts by interpreting geological structures and comparing them with other people's cases as well. Scientific explanatory hypotheses were mainly found according to the standard of hypothetical types, which can be constructed that those participants tried to explain and apply established knowledge and preconception.

• Journal of the Korean earth science society
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• v.39 no.5
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• pp.501-515
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• 2018

In this study, the method of multiple working hypotheses (MMWH) as a method of earth scientific inquiry was applied in a context of abductive reasoning about the formation of a rock with a specific structure, and the characteristics of MMWH revealed in the reasoning process were explored. Participants were 31 senior undergraduate students enrolled in a course in a university of education. As part of the course, the participants performed abductive inquiry with multiple working hypotheses about the formation of a rock. The students were asked to record both the processes and results of their reasoning in sketchbooks. The content of the students' sketchbook reports was analyzed according to the principle of analytic induction. Results demonstrated four assertions. First, the participants' working hypotheses were suggested in the use of resource models, and the adaption of the resource models often occurred in this process. Second, the perceptual properties of evidence influenced the activation of the resource models. Third, the kinds of observed evidence and the different interpretations of evidence resulted into different judgments on working hypotheses. Fourth, sometimes new hypotheses were generated by the combination of alternative hypotheses. Implications of these findings for earth science education and relevant research were discussed.

• Journal of Distribution Science
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• v.15 no.2
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• pp.121-132
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• 2017

Purpose - There are many variables related to consumer-brand relationship such as brand attitude, attachment, commitment and brand loyalty and we should manage these all variables successfully to achieve a strong brand loyalty. The objective of this research is to investigate the path from brand attitude and brand attachment to brand commitment and brand loyalty. Specially, this article focuses on the moderating effects of brand type and consumer innovativeness in the causal relationships between variables. Research design, data, and methodology - The seven hypotheses were proposed and tested empirically in this research. Three of seven hypotheses were the effects of brand attitude and brand attachment on the brand commitment and brand loyalty. Another two hypotheses presented the moderating effect of brand type and other two hypotheses expressed the moderating effect of consumer innovativeness in the causal relationships between variables. Research data were collected from the surveying of university students and the 282 samples were used to test the proposed hypotheses empirically. We utilized SPSS 20.0 and AMOS 20.0 for statistical analyses such as reliability test, validity test and path analysis. Results - The results show that brand attitude influences the brand loyalty and brand attachment influences the brand commitment positively. The brand attachment also influences the brand commitment positively. We found that there is a moderating effect of brand type in the causal relationship between brand attitude and loyalty though there is no significant moderating effect in the causality between brand attachment and commitment. We also fount that there is no significant moderating effect of consumer innovativeness in the causal relationships among brand attitude, brand attachment, brand commitment and brand loyalty. In Summary, 5 of 7 hypotheses in this study were supported and 2 hypotheses were not supported. Conclusions - There is a path model of consumer-brand relationship from brand attitude and brand attachment to brand commitment and brand loyalty. Companies should provide their consumers with effective marketing program in every phase of consumer-brand relationship to build brand loyalty. In addition, there are possibilities that the relationships among brand attitude, brand attachment, brand commitment and brand loyalty are moderated by brand type and consumer innovativeness. Companies should consider perceived brand type and innovativenss of their consumers in planning and executing their various marketing programs for their brand management.

• Journal of KIISE:Software and Applications
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• v.29 no.4
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• pp.270-277
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• 2002

Text filtering is a task of deciding whether a document has relevance to a specified topic. As Internet and Web becomes wide-spread and the number of documents delivered by e-mail explosively grows the importance of text filtering increases as well. The aim of this paper is to improve the accuracy of text filtering systems by using machine learning techniques. We apply AdaBoost algorithms to the filtering task. An AdaBoost algorithm generates and combines a series of simple hypotheses. Each of the hypotheses decides the relevance of a document to a topic on the basis of whether or not the document includes a certain word. We begin with an existing AdaBoost algorithm which uses weak hypotheses with their output of 1 or -1. Then we extend the algorithm to use weak hypotheses with real-valued outputs which was proposed recently to improve error reduction rates and final filtering performance. Next, we attempt to achieve further improvement in the AdaBoost's performance by first setting weights randomly according to the continuous Poisson distribution, executing AdaBoost, repeating these steps several times, and then combining all the hypotheses learned. This has the effect of mitigating the ovefitting problem which may occur when learning from a small number of data. Experiments have been performed on the real document collections used in TREC-8, a well-established text retrieval contest. This dataset includes Financial Times articles from 1992 to 1994. The experimental results show that AdaBoost with real-valued hypotheses outperforms AdaBoost with binary-valued hypotheses, and that AdaBoost iterated with random weights further improves filtering accuracy. Comparison results of all the participants of the TREC-8 filtering task are also provided.