• The Journal of Korean Association of Computer Education
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• v.8 no.6
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• pp.85-94
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• 2005

Today the web-based learning management information systems are developed as forms of many ordered learning information systems acting up to the personal characteristics, but the previous systems have difficulty in their mutual working, maintaining, and repairing between the systems, because of their one-sided "push" method or reuse of the contents. Also they were not managed together having the dissimilar learning management systems each. Therefore, I made up for the weak points of the previous systems and embodied the international standard SCORM-based ordered learning management information systems. After adapting to sequencing contents, all systems are supplied; giving lectures, solving the problems, evaluating the learners and the function of successive personalization learning based on the result of the learner evaluation systems.

• Journal of Powder Materials
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• v.23 no.2
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• pp.149-169
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• 2016

Additive manufacturing (AM) is defined as the manufacture of three-dimensional tangible products by additively consolidating two-dimensional patterns layer by layer. In this review, we introduce four fundamental conceptual pillars that support AM technology: the bottom-up manufacturing factor, computer-aided manufacturing factor, distributed manufacturing factor, and eliminated manufacturing factor. All the conceptual factors work together; however, business strategy and technology optimization will vary according to the main factor that we emphasize. In parallel to the manufacturing paradigm shift toward mass personalization, manufacturing industrial ecology evolves to achieve competitiveness in economics of scope. AM technology is indeed a potent candidate manufacturing technology for satisfying volatile and customized markets. From the viewpoint of the innovation technology adoption cycle, various pros and cons of AM technology themselves prove that it is an innovative technology, in particular a disruptive innovation in manufacturing technology, as powder technology was when ingot metallurgy was dominant. Chasms related to the AM technology adoption cycle and efforts to cross the chasms are considered.

• Journal of Korean Elementary Science Education
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• v.27 no.2
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• pp.158-169
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• 2008

The purpose of this study was to analyze the uses of the external representations in the $3{\sim}6th$ grade science textbooks developed under the 7th National Curriculum on the basis of the theories and the research results on learning with the multiple representations. The results showed that the frequencies of the macroscopic external representations were higher than those of the symbolic external representations. The external representations with drawing and/or writing, especially writing, were used more frequently than those without drawing and/or writing. However, the most of the external representations were rarely used according to the principles and/or the theories (e.g., personalization principle, dual coding theory, cognitive load theory, and social constructivism theory) for effective uses of the multiple external representations in the science textbooks. The present study provides the guideline to establish the effective uses of the external representations in the science textbooks that not only meet learners but also teachers.

• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.279-284
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• 2018

With the availability of real-time student behavioral data, personalization on education is gaining a huge traction. Data collected from massively open online courses (MOOC) has shifted the content delivery method from fixed, static to user-adopted form. Such educational content can be personalized by student's level of achivement. In this paper, we propose a service that automates the content restructuring, based on dynamic profile. With the student behavioral data, the proposed service restructures educational content by changing the order, extending and shrinking the published material. To do this, we record students' behavioral data and content information as a metadata, which will be used to generate dynamic profile.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.177-191
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• 2003

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is the most successful recommendation technology. Web usage mining and clustering analysis are widely used in the recommendation field. In this paper, we propose several hybrid collaborative filtering-based recommender procedures to address the effect of web usage mining and cluster analysis. Through the experiment with real e-commerce data, it is found that collaborative filtering using web log data can perform recommendation tasks effectively, but using cluster analysis can perform efficiently.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.239-251
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• 2014

The personal cloud is a service which approaches the personal contents scattering to all terminals and online space at the personal information-oriented age in which the person's digital device including the Smart-phone, MID, PC, IPTV and etc, increases and the personal online service including the blog, E-mail, UCC, social network service and ect, rapidly increasing for the cloud computing regardless of the terminal anywhere independently and can provide the high vale added personalization service through the analysis of the contents and processing. The future of the personal cloud market is prospected through the service and technology which enhances the understanding about the cloud computing, that is the next generation IT paradigm which the world IT companies pay attention to, and is provided and business strategy analysis of the security and normalizing and related companies.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.161-167
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• 2021

This paper proposes a new personalized HRTF estimation method which is based on a deep neural network (DNN) model and improved elevation reproduction using a notch filter. In the previous study, a DNN model was proposed that estimates the magnitude of HRTF by using anthropometric measurements [1]. However, since this method uses zero-phase without estimating the phase, it causes the internalization (i.e., the inside-the-head localization) of sound when listening the spatial sound. We devise a method to estimate both the magnitude and phase of HRTF based on the DNN model. Personalized HRIR was estimated using the anthropometric measurements including detailed data of the head, torso, shoulders and ears as inputs for the DNN model. After that, the estimated HRIR was filtered with an appropriate notch filter to improve elevation reproduction. In order to evaluate the performance, both of the objective and subjective evaluations are conducted. For the objective evaluation, the root mean square error (RMSE) and the log spectral distance (LSD) between the reference HRTF and the estimated HRTF are measured. For subjective evaluation, the MUSHRA test and preference test are conducted. As a result, the proposed method can make listeners experience more immersive audio than the previous methods.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.175-181
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• 2024

The paper examines the impact of digital media art on urban creative products, analyzing opportunities and challenges in the digital era. It emphasizes the development of urban cultural and creative products, highlighting their significance and future growth potential. The digital media era provides unprecedented innovation opportunities, utilizing advanced tools for efficient design, production, and marketing. Trends like personalization, customization, AI, and big data offer new expressions and market prospects. Cultural products evolve in design, marketing, and sales channels due to digital media, with tools like social media and e-commerce platforms opening new promotion avenues. Case studies illustrate digital media's role in driving innovation and enhancing user experiences. The paper addresses challenges in market competition, copyright, and technological renewal, while recognizing opportunities from AI and big data. The creative industries must adapt and innovate to remain relevant. Looking ahead, urban creative products will evolve under digitalization, relying on digital means to attract consumers and enhance brand value. Cultural products, beyond economic entities, disseminate urban culture and creative spirit. In the digital era, urban creative products demonstrate potential and necessity, prompting a reevaluation of digital technology's role. Through continuous innovation, this field contributes to cultural and economic levels, impacting urban characteristics and heritage. Urban creative products play an increasingly vital role in the global cultural and creative economy.

• The Journal of Industrial Distribution & Business
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• v.11 no.2
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• pp.49-59
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• 2020

Purpose: Online and social media and mobile shopping are increasing and companies are required to provide personal information in order to supplement the non-invasive characteristics of the channels. With the increased provision of personal information, consumers' personal and social concerns about the prevention of personal information infringement are also increasing, and in response, personal or opt-in marketing has emerged to compensate for reckless information abuse. Despite the background of this emergence, the existing prior studies are limited to ignoring the negative feelings of consumers in the real world, including only the net function and positive effect of the opt-in mail. Research design, data and methodology: The research framework was intended to utilize the impact of human marketing activities on consumer attitudes combined with positive and negative factors. Factors that positively affect attitudes toward permation marketing were presented, such as informality, and perceived risks were presented as negative impact factors. Also, based on previous prior research, the prior factors of opt-in marketing were to present the effect on purchase intent through the medium of attitude toward opt-in marketing. Results: In this study, we used the framework of a two factor theory to address positive and negative factors as a leading factor in the customer attitude toward opt-in mail advertising, and as a result, functionality and personalization have a positive effect on customer attitude and perceived risk have a negative impact on customer attitude. In addition, it was confirmed that the customer attitude formed this way affects the intention to purchase again. Conclusions: This study suggests that we have demonstrated that marketing, an opt-in marketing that has been recognized as part of marketing that is deployed after obtaining customer consent, has been applied without any other marketing methodology. E-mail advertising at this point also provides practical implications that the system safeguards are in place under an opt-in protocol or system, and that even if an e-mail advertisement is carried out, customers will need to look at the level of awareness about the risks, and suggests that they need to consider the customer's journey that could lead to purchase at the content level.

• The Journal of Society for e-Business Studies
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• v.10 no.2
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• pp.109-132
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• 2005

With the flood of multimedia contents over the digital TV channels, the internet, and etc., users sometimes have a difficulty in finding their preferred contents, spend heavy surfing time to find them, and are even very likely to miss them while searching. In this paper we suggests two-step clustering technique using time schema on how the system can recommend the user's preferred contents based on the collaborative filtering that has been proved to be successful when new users appeared. This method maps and recommends users' profile according to the gender and age at the first step, and then recommends a probabilistic item clustering customers who choose the same item at the same time based on time schema at the second stage. In addition, this has improved the accuracy of predictions in recommendation and the efficiency in time calculation by reflecting feedbacks of the result of the recommender engine and dynamically update customers' preference.