• Journal of Computing Science and Engineering
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• v.6 no.4
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• pp.243-256
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• 2012

In this paper we present a framework for a novel kind of context-aware preference query composition whereby queries for the Preference SQL system are created. We choose a commercial e-business platform for outdoor activities as a use case and develop a context model for this domain within our framework. The suggested model considers explicit user input, domain-specific knowledge, contextual knowledge and location-based sensor data in a comprehensive approach. Aside from the theoretical background of preferences, the optimization of preference queries and our novel generator based model we give special attention to the aspects of the implementation and the practical experiences. We provide a sketch of the implementation and summarize our user studies which have been done in a joint project with an industrial partner.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.100.2-100
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• 2002

In this paper we propose a new visual servoing method for door opening with mobile manipulator. We use an eye-to-hand system that stereo camera is mounted on mobile platform, and adopt the position-based method. The previous methods for door opening mostly used eye-in-hand system with mono camera and required predefined knowledge such as radius and position about door grip, which was mainly caused by using mono cam era. This is also a severe constraint for pursuing general-purpose algorithm for door opening. For overcoming such drawback, we use stereo camera and suggest a new method that detect the door grip and estimate its pose from stereo depth information without predefined knowledge. Al...

• Knowledge Management Research
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• v.21 no.2
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• pp.177-196
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• 2020

Since the adoption of K-IFRS(Korean International Financial Reporting Standards), the amount of financial footnotes has been increased. However, due to the stereotypical phrase and the lack of conciseness, deriving the core information from footnotes is not really easy yet. To propose a solution for this problem, this study tried financial footnote analysis for financial ratio predictions based on text-mining techniques. Using the financial statements data from 2013 to 2018, we tried to predict the earning per share (EPS) of the following quarter. We found that measured prediction errors were significantly reduced when text-mined footnotes data were jointly used. We believe this result came from the fact that discretionary financial figures, which were hardly predicted with quantitative financial data, were more correlated with footnotes texts.

• Asia Marketing Journal
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• v.19 no.4
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• pp.61-85
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• 2018

Open innovation is based on a different knowledge landscape, with a different logic about the sources and uses of technologies. It implies that firms increasingly rely on external sources of innovation by emphasizing these ideas and resources. Using datasets from the UK biotechnology industry, this paper explores firms' willingness to participate in open innovation. The results indicate that the switching cost is identified as the direct predictor of the willingness to participate in open innovation. While high commitment based on the trust with partner increases switching costs, IT infrastructure within the firm decreases switching costs to open innovation. Taken together, this research broadens the study on open innovation by applying the switching cost as a medium of knowledge transfer in the biotech industry.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.1
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• pp.341-360
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• 2015

This paper proposes a noble service architecture based on scientific infographic as well as semi-automatic knowledge process for 'KISTI's Scent of Science' database, which has been highly credited as a science journalism service in Korea. Unlike other specialized scientific databases for domain experts and scientists, the database aims at providing comprehensible and intuitive information about various important scientific concepts which may seem not to be easily understandable to general public. In order to construct a knowledge-base from the database, we deeply analyze the traits of the database and then establish a semi-automatic approach to identify and extract various scientific intelligence from its contents. Furthermore, this paper defines a scientific infographic service platform based on the knowledge-base by offering its detailed structure, methods and characteristics, which shows a progressive future direction for science journalism service.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.2
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• pp.73-83
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• 2011

In the manufacturing system, production-planning is very important in effective management for expensive production facilities and machineries. To enhance efficiency of Manufacturing Execution System(MES), a manufacturing system that reduces the difference between planning and execution, certain production-planning needs a dispatching rule that is properly designed for characteristic of work information and there should be a appropriate selection for the rule as well. Therefore, in this paper dispatching rule will be selected by several simulations based on characteristics of work information derived from process planning data. By constructing information that are from simulation into ontology, one of the knowledge-based-reasoning, production planning platform based on the selection of dispatching rule will be demonstrated. The platform has strength in its wider usage that is not limited to where it is applied. To demonstrate the platform, RacerPro and Prot$\acute{e}$g$\acute{e}$ are used in parts of ontology reasoning, and JAVA and FlexChart were applied for production-planning simulation.

• Knowledge Management Research
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• v.20 no.3
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• pp.173-193
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• 2019

Reward-based crowdfunding is a funding platform that allows funds to be raised to early operators who have lack of funds, and is seen as an outstanding infrastructure that is going to lead the fourth industrial revolution in that it is a field of realization of new technologies and creative ideas by start-ups. Reward-based crowdfunding has grown in line with the trend of the fourth industrial revolution, and funding success cases are taking place in various industries that culture/art to technology/IT, including as a new means of knowledge management in a rapidly changing industrial environment. The study focused on the fact that consumer's donation purposes may also vary depending on the category of projects classified as reward-based crowdfunding. Because consumer payment decisions and motivation of consumer purchasing behavior are classified according to the purpose of purchase, the previous papers that the goal gradient effect that the main motivation of consumer donation for reward-based crowdfunding introduced vary depending on project category of utilitarian and hedonic. In this study, consumer's daily donation data is collected by Indiegogo which is a leading reward-based crowdfunding company using web-crawling and the model was defined as propensity score matching (PSM) and random effect model. The results showed that the goal gradient effect occurred in utilitarian project category, but no goal gradient effect for the hedonic project category. Furthermore, this paper developed the study of motivation of consumer donation and contributes theoretical foundation by the results consumer donation may vary depending on the project category; also, this paper has implications for an effective marketing strategy depending on the project category leaves real meaning to the projector.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.235-246
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• 2011

We are living in the era of globalization, convergence of multi technologies and dual service system especially in the cultural, social, political, economical and technical aspects. These waves are making us go beyond Information Society and urge to enter the Smart Society. In the past, we have focused only on creating, proceeding and accumulating vast amount of information. Where as today, we are hoping to be provided with more intellectual, cheep, and converged form with the help of stored information without any space and time constraints. In this thesis, with consideration of the knowledge based society service integration paradigm, we will first look at the concept of Smart Service with various case studies, proposition strategy implementation on Smart Platform by analysing the service the implementing aspect, looking at the roles of each related divisions, and measuring the secure elements etc.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.75-80
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• 2018

In this paper, we propose heterogeneous lifelog mining model in health big-data platform. It is an ontology-based mining model for collecting user's lifelog in real-time and providing healthcare services. The proposed method distributes heterogeneous lifelog data and processes it in real time in a cloud computing environment. The knowledge base is reconstructed by an upper ontology method suitable for the environment constructed based on the heterogeneous ontology. The restructured knowledge base generates inference rules using Jena 4.0 inference engines, and provides real-time healthcare services by rule-based inference methods. Lifelog mining constructs an analysis of hidden relationships and a predictive model for time-series bio-signal. This enables real-time healthcare services that realize preventive health services to detect changes in the users' bio-signal by exploring negative or positive correlations that are not included in the relationships or inference rules. The performance evaluation shows that the proposed heterogeneous lifelog mining model method is superior to other models with an accuracy of 0.734, a precision of 0.752.

• Smart Media Journal
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• v.8 no.2
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• pp.66-73
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• 2019

Recently, object recognition technology using computer vision has attracted attention as a technology to replace sensor-based object recognition technology. It is often difficult to commercialize sensor-based object recognition technology because such approach requires an expensive sensor. On the other hand, object recognition technology using computer vision may replace sensors with inexpensive cameras. Moreover, Real-time recognition is viable due to the growth of CNN, which is actively introduced into other fields such as IoT and autonomous vehicles. Because object recognition model applications demand expert knowledge on deep learning to select and learn the model, such method, however, is challenging for non-experts to use it. Therefore, in this paper, we analyze the structure of deep - learning - based object recognition models, and propose a platform that can automatically select a deep - running object recognition model based on a user 's desired condition. We also present the reason we need to select statistics-based object recognition model through conducted experiments on different models.