• Journal of Digital Convergence
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• v.19 no.6
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• pp.251-258
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• 2021

In this paper, we proposed an encoder-decoder model utilizing residual learning to improve the accuracy of the U-Net-based semantic segmentation method. U-Net is a deep learning-based semantic segmentation method and is mainly used in applications such as autonomous vehicles and medical image analysis. The conventional U-Net occurs loss in feature compression process due to the shallow structure of the encoder. The loss of features causes a lack of context information necessary for classifying objects and has a problem of reducing segmentation accuracy. To improve this, The proposed method efficiently extracted context information through an encoder using residual learning, which is effective in preventing feature loss and gradient vanishing problems in the conventional U-Net. Furthermore, we reduced down-sampling operations in the encoder to reduce the loss of spatial information included in the feature maps. The proposed method showed an improved segmentation result of about 12% compared to the conventional U-Net in the Cityscapes dataset experiment.

• The Journal of Society for e-Business Studies
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• v.15 no.2
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• pp.167-189
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• 2010

As adopting ubiquitous technology into civil engineering, new city model is suggested called U-City. This paper proposes the framework of U-City management center to support effective services operation. The aims of the framework are to provide the development and operation environment for U-City services. Basically, these objectives are achieved by adopting the semantic web service technology to the framework. In this paper, OWL-S is mainly conducted to represent the description of U-City services. In addition, this paper insists that fine grained unit services are required to guarantee reusability, compatibility, and scalability of the services on U-City management center. The documentations conducted by OWL-S are provided as an example of service descriptions. At the last section, this paper also presents the architecture of U-City management center which enables automatic service discovery, selection, composition and interoperation.

• The KIPS Transactions:PartA
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• v.13A no.1 s.98
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• pp.87-94
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• 2006

Internet makes it possible to access anytime, anywhere learning and so many LMS(Learning Management Systems) serve web based learning. But LMS has not flexible and qualified metadata to offer customired teaming. So we need extensible and flexible techniques which make if possible to define and share advanced teaming metadata. This paper presents an approach for implementing advanced learning metadata in LMS using RDF and the Semantic Web language. So we will first sketch the learning scenario in Semantic Web environment and structure of metadata management. Next we suggest two types of RDF authoring tool and search RDF documents. Advanced metadata management techniques enables the organization of learning materials around small pieces of semantically annotated learning objects. With these metadata learner can customize learning courses, improve retrieval performances.

• Korean Association of Arts Management
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• no.58
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• pp.205-239
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• 2021

This study explores the importance of semantic web-based network construction in art data archiving, as well as its meaning and value in the context of arts management along with its potential for future application. The study focuses on oral history obtained from the Arko Arts Archives that contained records of the lives and artistic views of early artists. In this study, the possibility of applying semantic web-based technology to materials concerning culture and the arts was discussed in five aspects based on the results of the case analysis. First, checking the relationship and discovering hidden artists are possible by revealing relationships between characters. Second, understanding and studying society and culture at a given time is possible by interpreting the contextual meaning of information. Third, art exploration can be done broadly and deeply, encompassing various genres from the perspective of the consumer. Fourth, through art construction, history can be reconstructed using a new and rich method. Fifth, expanding the scope beyond the boundaries of art is possible through convergence and collaboration of programs that handle big data. The network data can be used in various methods, such as art history research, art planning, and creation, throughout the art ecosystem. The results of the study suggest that digitizing a large quantity of data concerning culture and the arts is meaningful in arts management as well as identifying and analyzing the relationship network among data clusters using semantic web-based technology.

• Journal of Computing Science and Engineering
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• v.2 no.1
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• pp.1-25
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• 2008

Spoken dialog tasks incur many errors including speech recognition errors, understanding errors, and even dialog management errors. These errors create a big gap between the user's intention and the system's understanding, which eventually results in a misinterpretation. To fill in the gap, people in human-to-human dialogs try to clarify the major causes of the misunderstanding to selectively correct them. This paper presents a method of clarification techniques to human-to-machine spoken dialog systems. We viewed the clarification dialog as a two-step problem-Belief confirmation and Clarification strategy establishment. To confirm the belief, we organized the clarification process into three systematic phases. In the belief confirmation phase, we consider the overall dialog system's processes including speech recognition, language understanding and semantic slot and value pairs for clarification dialog management. A clarification expert is developed for establishing clarification dialog strategy. In addition, we proposed a new design of plugging clarification dialog module in a given expert based dialog system. The experiment results demonstrate that the error verifiers effectively catch the word and utterance-level semantic errors and the clarification experts actually increase the dialog success rate and the dialog efficiency.

• Culinary science and hospitality research
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• v.24 no.3
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• pp.167-176
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• 2018

In this study, Big Data was used to collect the information related to 'Cooking Academy' keywords. After collecting all the data, we calculated the frequency through the text mining and selected the main words for future data analysis. Data collection was conducted from Google Web and News during the period from January 1, 2013 to December 31, 2017. The selected 64 words were analyzed by using UCINET 6.0 program, and the analysis results were visualized with NetDraw in order to present the relationship of main words. As a result, it was found that the most important goal for the students from cooking school is to work as a cook, likewise to have practical classes. In addition, we obtained the result that SNS marketing system that the social sites, such as Facebook, Twitter, and Instagram are actively utilized as a marketing strategy of the institute. Therefore, the results can be helpful in searching for the method of utilizing big data and can bring brand-new ideas for the follow-up studies. In practical terms, it will be remarkable material about the future marketing directions and various programs that are improved by the detailed curriculums through semantic network of cooking school by using big data.

• IE interfaces
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• v.24 no.3
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• pp.248-257
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• 2011

The amount of Meta-data to be managed increases with development of information technology. However, when trying to integrate and share product information of heterogeneous systems within or between companies, sharing of information is impossible if product information classification systems are different. Due to the situation mentioned above, engineers judge the product information classification system and maps corresponding Meta-data for document-based sharing. Judging exponentially increasing amount of data by engineers and sharing product information using documents create great amount of time delay and errors in data handling. Therefore, construction of a system for integrated management and interoperability between product information based on semantic information similar to engineer's judgment is required. This paper proposes a methodology and necessity of a system for interoperability of product information based on semantic web, and also designs a system to integrate heterogeneous systems with different product information using rule based reasoning. This paper also suggests a system base for interoperability and integration of product information between heterogeneous systems by integrating the product information classification system semantically.

• Journal of the Korean Society for information Management
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• v.21 no.4 s.54
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• pp.251-263
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• 2004

For question-answering system, question analysis module finds the question points from user's natural language questions, classifies the question types, and extracts some useful information for answer. This paper proposes a question type classifying technique based on focus words extracted from questions and word semantic information, instead of complicated rules or huge knowledge resources. It also shows how to find the question type without focus words, and how useful the synonym or postfix information to enhance the performance of classifying module.

• Journal of the Korean Society for information Management
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• v.26 no.4
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• pp.7-34
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• 2009

Structures of stories, paragraphs, and sentences and inferences applied to indexing and searching were studied to construct the full-text and sentence retrieval system for storytelling. The system designed the database of stories, paragraphs, and sentences and the knowledge-base of inference rules to aid to write the story. The Knowledge-base comprised the files of story frames, paragraph scripts, and sentence logics made by mark-up languages like SWRL etc. able to operate in semantic web. It is necessary to establish more precise indexing language represented the sentences and to create a mark-up languages able to construct more accurate inference rules.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.1
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• pp.1-8
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• 2020

The purpose of this study is to understand the impact of integrated art therapy on the life meaning of new firefighters. The subjects of the study selected 108 newfirefighters to be hired as part of the new training center in South Gyeongsang Province. Out of 108 participants, consisting of 54 volunteers, conducted an integrated art therapy program once a week, 60 minutes each, and eight sessions. Research tools carried out pre - and post-inspection testing using a scale of meaning in life. For data analysis, t-test was conducted using SPSS 22.0 program.The study found that integrated art therapy using semantic therapy had statistically significant differences in meaning in life.The experimental group has made positive changes in its pursuit of meaning, finding meaning, and discovering meaning in life rather than in control groups. In other words, the new fire fighting public officials' ability to positively change in difficult situations, adapt to the environment, and overcome difficulties was improved. In particular, he showed changes such as realizing the importance of people and life, clarity of purpose and efforts to change himself. Therefore, it is necessary to carefully review the fire school program aimed at fostering firefighters with a balanced body.