• Journal of Science and Technology Studies
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• v.19 no.1
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• pp.53-90
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• 2019

The purpose of this study is to analyze a case of social problem- solving R&D project. The case is about a food waste disposer system built in housing complex The characteristics revealed through the case study are as follows. It has been performed by the 'Connect and Solution Development (C&SD)' strategy that could shorten the whole innovation process. In order to increase the economic and social acceptability of the system, researchers formed a dense network with citizens, firms and local governments and tried to improve existing law to make new markets for the system. Instead of developing a new system in a top-down approach, they chose to use existing technologies and entities to address social problems quicker and easier.

• Journal of the Korean Society for information Management
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• v.33 no.1
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• pp.7-32
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• 2016

This study has measured interdisciplinarity using a topic modeling, which automatically extracts sub-topics based on term information appeared in documents group unlike the traditional top-down approach employing the references and classification system as a basis. We used titles and abstracts of the articles published in top 20 journals for the past five years by the 5-year impact factor under the category of 'Information & Library Science' in JCR 2013. We applied 'Discipline Diversity' and 'Network Coherence' as factors in measuring interdisciplinarity; 'Shannon Entropy Index' and 'Stirling Diversity Index' were used as indices to gauge diversity of fields while topic network's average path length was employed as an index representing network cohesion. After classifying the types of interdisciplinarity with the diversity and cohesion indices produced, we compared the topic networks of journals that represent each type. As a result, we found that the text-based diversity index showed different ranking when compared to the reference-based diversity index. This signifies that those two indices can be utilized complimentarily. It was also confirmed that the characteristics and interconnectedness of the sub-topics dealt with in each journal can be intuitively understood through the topic networks classified by considering both the diversity and cohesion. In conclusion, the topic modeling-based measurement of interdisciplinarity that this study proposed was confirmed to be applicable serving multiple roles in showing the interdisciplinarity of the journals.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.185-207
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• 2022

Recently, with the development of deep learning technology, researches to apply a deep learning algorithm to analyze unstructured data such as text and images are being actively conducted. Text classification has been studied for a long time in academia and industry, and various attempts are being performed to utilize data characteristics to improve classification performance. In particular, a hierarchical relationship of labels has been utilized for hierarchical classification. However, the top-down approach mainly used for hierarchical classification has a limitation that misclassification at a higher level blocks the opportunity for correct classification at a lower level. Therefore, in this study, we propose a methodology for classifying hierarchical data using the autoencoder-based deeply supervised network that high-level classification does not block the low-level classification while considering the hierarchical relationship of labels. The proposed methodology adds a main classifier that predicts a low-level label to the autoencoder's latent variable and an auxiliary classifier that predicts a high-level label to the hidden layer of the autoencoder. As a result of experiments on 22,512 academic papers to evaluate the performance of the proposed methodology, it was confirmed that the proposed model showed superior classification accuracy and F1-score compared to the traditional supervised autoencoder and DNN model.

• Journal of Environmental Policy
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• v.7 no.4
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• pp.105-134
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• 2008

Local opposition and protest constitute single greatest hurdle to the siting of locally unwanted land uses(LULUs), especially siting of high-level radioactive disposal not only throughout Korea but also throughout the industrialized world. It can be attributed mainly to the NIMBYism, equity problem, and lack of participation. These problems are arisen from rational planning process which emphasizes instrumental rationality. But planning is a value-laden political activity, in which substantive rationality is central. To achieve this goals, we need a sound planning process for siting LULUs, which should improve the ability of citizens to influence the decisions that affects them. By a sound planning process, we mean one that is open to citizen input and contains accurate and complete information. In other word, the public is also part of the goal setting process and, as the information and analyses developed by the planners are evaluated by the public, strategies for solutions can be developed through consensus-building. This method is called as a co-operative siting process, and must be structured in order to arrive at publicly acceptable decisions. The followings are decided by consensus-building method. 1. Negotiation will be held? 2. What is the benefits and risks of negotiation? 3. What are solutions when collisions between national interests and local ones come into? 4. What are the agendas? 5. What is the community' role in site selection? 6. Are there incentives to negotiation. 7. Who are the parties to the negotiation? 8. Who will represent the community? 9. What groundwork of negotiation is set up? 10. How do we assure that the community access to information and expert? 11. What happens if negotiation is failed? 12. Is it necessary to trust each other in negotiations? 13. Is a mediator needed in negotiations?