• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.611-618
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• 2003

In this paper, we describe a fuzzy inference approach for detecting and classifying shot transitions in video sequences. Our approach basically extends FAM (Fuzzy Associative Memory) to detect and classify shot transitions, including cuts, fades and dissolves. We consider a set of feature values that characterize differences between two consecutive frames as input fuzzy sets, and the types of shot transitions as output fuzzy sets. The inference system proposed in this paper is mainly composed of a learning phase and an inferring phase. In the learning phase, the system initializes its basic structure by determining fuzzy membership functions and constructs fuzzy rules. In the inferring phase, the system conducts actual inference using the constructed fuzzy rules. In order to verify the performance of the proposed shot transition detection method experiments have been carried out with a video database that includes news, movies, advertisements, documentaries and music videos.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.121-129
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• 2021

In addition to yellow dust occurring in China, fine dust has become a hot topic in Korea through news and media. Although there is fine dust generated from the outside, the purchase rate of air purifier products is increasing as external fine dust flows into the inside. The air purifier uses a filter internally, and the sensor notifies the user through the LED alarm whether the filter is replaced. However, there is currently no product measuring how much the filter rate is reduced and determining the pressure of the blower to operate. Therefore, in this paper, data are generated directly using Arduino, fine dust sensor, and differential pressure sensor. In addition, a program was developed using Python programming to calculate how old the filter is and to analyze the wind power of the blower according to the filter rate by calculating the measured dust and pressure values.

• Journal of English Language & Literature
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• v.54 no.1
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• pp.3-29
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• 2008

Ian Watt, author of The Rise of the Novel, maintained that the novel originated in modern England, came from prose discourses such as the news, political essays and journalistic writing which propagated the Enlightenment, and the novels represent formal realism. The main point of this paper is to examine Watt's theory of the rise of the novel on the basis of the criticism of antinomy of the Enlightenment and "the public sphere" in Habermas' terms. At first, I will criticize formal realism, which is not a new literary species, but a formally renovated realistic form that represented capitalism and protestantism. And, then, I will show that formal realism is a kind of antinomy because it turned away from the voices and reality of the low-class and women though the novel concentrated on common people, not the aristocrats. Secondly, I will inquire into the antinomy of the Enlightenment in the aspects of reason, freedom, individualism and women. In my view, as soon as the high-middle class acquired their political rights, these values were no more encouraged and the result revealed antinomy of the Enlightenment more explicitly. Thirdly, I'd argue that "the public sphere" had positive meanings to everyone when the bourgeosie were fighting against the Absolutism and the aristocracy. I'll also insist that the high-middle class and the intellectuals were in "the public sphere" in which Habermas argues that rationality and equality were thought to have been realized, while the low-middle class and most women were de-enlightened and disciplined by reading the novel privately. In conclusion, formal realism is not the rise of the novel, but the opening of the novel peculiar to bourgeosie parliamentarism from the middle-eighteenth century to the middle-twentieth century.

• Journal of English Language & Literature
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• v.57 no.1
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• pp.27-49
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• 2011

This article proposes to stay away from contemporary critical arguments concerning Michael's value system, which is construed mainly from his choice between his patrimonial lands and his son Luke. Presuming that Michael's value system as have been argued so far could never be the poet Wordsworth's own concern at the time of the composition of the poem "Michael," this article proposes to get back to the all too real socio-historical situation of the early nineteenth-century England. Mere consideration of the socio-historical situation, when combined with a close reading of the poetic text (a close reading of both the poetic story and the poetic history from which the story may be said to have been constructed), directs us to the poet working on the simple paradigm of 'the country and the city at war with each other' but the victory having been given to the city already. The guarantee contract for a supposedly prospering nephew's debt and the letter from another prospering relative in London are undoubtedly the key elements that lead us to the war paradigm. Michael's family members, each and all including Michael himself, and all of their village people, have been imbued with the city's commercial values, which renders them all the more easier victims within the war context. Luke's defeat in the city is viewed as being really the consequence, rather than the cause, of Michael's defeat, which became apparent as soon as the news of the latter's financial disaster reached his ear. Michael should therefore be regarded as one of the typical English countryfolk of the time, with whom Wordsworth often, but not always, identifies himself. Insofar as the economic view or attitude is concerned, there certainly is a distance between Michael and Wordsworth, this article argues.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.41-49
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• 2022

Documents often contain relative time expressions, and it is important to define a schema of the relative time information and develop a system that extracts such information from corpus. In this study, to deal with the relative time expressions, we propose seven additional attributes of timex3: year, month, day, week, hour, minute, and second. We propose a way to represent normalized values of the relative time expressions such as before, after, and count, and also design a set of rules to extract the relative time information from texts. With a new corpus constructed using the new attributes that consists of dialog, news, and history documents, we observed that our rule-set generally achieved 70% accuracy on the 1,041 documents. Especially, with the most frequently appeared attributes such as year, day, and week, we got higher accuracies compared to other attributes. The results of this study, our proposed timex3 attributes and the rule-set, will be useful in the development of services such as question-answer systems and chatbots.

• Educational Technology International
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• v.11 no.2
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• pp.77-103
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• 2010

The purpose of this study was to develop an instructional model using new technologies aiming to secure students' learnability and to enhance the public school values in the rural districts. The present study attempts to suggest a practical e-learning instructional and learning model named Group Dynamics- based Debate Instructional Model', which utilizes unique technology environment conditions in most. To develop the model, concepts of group dynamics and debate-based instructional models were reviewed. And in-service teachers in two public schools in a certain rural district were interviewed in order to collect and analyze their needs for a teaching and learning model with which they utilizes unique technology conditions as environment in most. Based on literature review and the need analysis, a group dynamics-based debate instructional model has been suggested in terms of conceptual model. And then expert assessment composing of five in-service teachers from the model schools was implemented twice in order to acquire the suggested model validation, followed by the model validation by a group of experts. Then a revised group dynamics-based debate instructional model has been finally suggested. The group dynamics-based debate instructional model is expected to build up members' affective connection in the process, to generate group value, or collective intelligence, and to establish positive discussion culture. Furthermore, beyond of just utilizing the existing materials, learners are encouraged to develop and collect their own materials and data such as expert's interview, or public news for their argument or refutation. In doing so, learners enhance their learnability as well as accountability, prompting self-directed learning, and establishing appropriate discussion culture resulting in positive learning outcomes.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.4
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• pp.299-307
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• 2024

Fishing villages, which are the focus of this study, are interested in fishing tourism for creating a new income base and sustaining fishing communities. This is because the extraordinary nature of the fishing village space creates new values in line with the function of tourism, however, the related policies are less than adequate compared to the importance of fishing village tourism. Therefore, this study aims to analyze the interest of Korean society in fishing village tourism the manner in which this issue has changed over time. Using the news analysis system, BigKinds, we systematically collected and analyzed articles related to fishing village tourism reported in the domestic media. The results showed that social interest in fishing village tourism and government policy support had increased over time, suggesting that fishing village tourism was an important strategy that could revitalize local economies and prevent the disappearance of fishing villages.

• Korean journal of communication and information
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• v.67
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• pp.209-240
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• 2014

This study observes reporters' awareness on the use of social media and their Core Values by using the Theory of Means-End Chain and drawing conclusions from a Hierarchical Value Map (HVM). In order to analyze reporters' knowledge and awareness on the use of social media via the laddering method, in-depth interviews of 46 reporters were conducted. The study showed that reporters consider sense of kinship, well-balanced understanding, and the desire for knowledge to be important Core Values. The results revealed that the convenience in interpersonal communication, development of intimacy in relationships, entertainment and affection, curiosity, the reduction in the cost of the acquisition of information, understanding of trends in issues, a peek into new information and the maintenance of interests, psychological dependability, and quick updates on information items are considered important Consequences of social media. In the Attributes level, the ability to write postings and links on Facebook and readability and 'follow' and 'mention' features on Twitter were confirmed to be important items in social media. The findings infer that reporters that make use of social media use Twitter and Facebook to build a sense of kinship with other users and gain well-balanced understanding by accessing a lot of information through social media. While this study examined the level of reporters' familiarity with the use of social media via the laddering method, the results cannot be seen as a generalization, as the interviewees were reporters from only the major news organizations.

• Journal of The Korean Association For Science Education
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• v.41 no.1
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• pp.59-70
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• 2021

Ability to make informed decisions by critically evaluating media information on socioscientific issues (SSI) is one of the crucial elements of scientific literacy that citizens should obtain. This study aims to investigate how middle school students evaluated media information about socioscientific issues (SSI) when they faced two different types of information (i.e., numerical and empathic information). To achieve the aim, 96 middle school students responded to the questionnaires asking them to evaluate reliability and persuasiveness of SSI media information. The questionnaires consisted of two sets of newspaper articles on each SSI (pro-numerical/empathic, against-numerical/empathic). After reading the articles, the students evaluated reliability and persuasiveness of each article and wrote the reasons for their evaluation. The results were as follows: First, the students believed that news articles with numerical information were more reliable than the ones with empathic information in all SSI contexts. They tended to trust scientific evidence and data from numerical information, and real cases, societal problems, expressions, and values from empathic information. In addition, they evaluated their reliability based on the logic of information, accuracy of information, breadth and depth of data, and quantity and quality of sources both numerical and empathic information. Second, in case of evaluating persuasiveness of the articles, they focused more on the values that information contained, richness and logicality of the information, rather than the types of information, regardless of the type of information.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.111-125
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• 2011

Clustering is a process of grouping similar or relevant documents into a cluster and assigning a meaningful concept to the cluster. By this process, clustering facilitates fast and correct search for the relevant documents by narrowing down the range of searching only to the collection of documents belonging to related clusters. For effective clustering, techniques are required for identifying similar documents and grouping them into a cluster, and discovering a concept that is most relevant to the cluster. One of the problems often appearing in this context is the detection of a complex concept that overlaps with several simple concepts at the same hierarchical level. Previous clustering methods were unable to identify and represent a complex concept that belongs to several different clusters at the same level in the concept hierarchy, and also could not validate the semantic hierarchical relationship between a complex concept and each of simple concepts. In order to solve these problems, this paper proposes a new clustering method that identifies and represents complex concepts efficiently. We developed the Hierarchical Overlapping Clustering (HOC) algorithm that modified the traditional Agglomerative Hierarchical Clustering algorithm to allow overlapped clusters at the same level in the concept hierarchy. The HOC algorithm represents the clustering result not by a tree but by a lattice to detect complex concepts. We developed a system that employs the HOC algorithm to carry out the goal of complex concept detection. This system operates in three phases; 1) the preprocessing of documents, 2) the clustering using the HOC algorithm, and 3) the validation of semantic hierarchical relationships among the concepts in the lattice obtained as a result of clustering. The preprocessing phase represents the documents as x-y coordinate values in a 2-dimensional space by considering the weights of terms appearing in the documents. First, it goes through some refinement process by applying stopwords removal and stemming to extract index terms. Then, each index term is assigned a TF-IDF weight value and the x-y coordinate value for each document is determined by combining the TF-IDF values of the terms in it. The clustering phase uses the HOC algorithm in which the similarity between the documents is calculated by applying the Euclidean distance method. Initially, a cluster is generated for each document by grouping those documents that are closest to it. Then, the distance between any two clusters is measured, grouping the closest clusters as a new cluster. This process is repeated until the root cluster is generated. In the validation phase, the feature selection method is applied to validate the appropriateness of the cluster concepts built by the HOC algorithm to see if they have meaningful hierarchical relationships. Feature selection is a method of extracting key features from a document by identifying and assigning weight values to important and representative terms in the document. In order to correctly select key features, a method is needed to determine how each term contributes to the class of the document. Among several methods achieving this goal, this paper adopted the $x^2$�� statistics, which measures the dependency degree of a term t to a class c, and represents the relationship between t and c by a numerical value. To demonstrate the effectiveness of the HOC algorithm, a series of performance evaluation is carried out by using a well-known Reuter-21578 news collection. The result of performance evaluation showed that the HOC algorithm greatly contributes to detecting and producing complex concepts by generating the concept hierarchy in a lattice structure.