• Journal of information and communication convergence engineering
• /
• v.1 no.2
• /
• pp.74-81
• /
• 2003

There are many researches on visual languages, but the most of them are difficult to support various collaborative interactions on a distributed multimedia environment. So, this paper suggests a collaborative visual language for interaction between multi-users. The visual language can describe a conceptual model for collaborative interactions between multi-users. Using the visual language, generated visual sentences consist of object icons and interaction operators. An object icon represents a user who is responsible for a collaborative activity, has dynamic attributes of a user, and supports flexible interaction between multi-users. An interaction operator represents an interactive relation between multi-users and supports various collaborative interactions. Merits of the visual language are as follows: supporting of both asynchronous interaction and synchronous interaction, supporting flexible interaction between multi-users according to participation or leave of users, supporting a user oriented modeling, etc. For example, an application to a workflow system for document approval is illustrated. So we could be found that the visual language shows a collaborative interaction.

• Convergence Security Journal
• /
• v.6 no.3
• /
• pp.127-133
• /
• 2006

Some Research is actively being done on a web-based collaborative learning system. This is changes in educational paradigm in the knowledge information age. A web-based collaborative learning system for question making is to improve the effect of studying through positive interactions between colleagues and to motivate studying through group competitions. This system is designed to active and self-leading studying when a learner do collaborative learning for question making in group. This system can help initiate and active studying to learner through a course of collaborative learning for question making. It can be used to achieve collaborative learning in various ways.

• Architectural research
• /
• v.7 no.1
• /
• pp.1-9
• /
• 2005

The needs of collaboration among design participants spread in different locations is emphasized in the early design stage in order to not only save time and provide places to meet and talk, but also to save cost for those events as well: This is being realized by the Internet, which helps support a networked, integrated real-time multi-user environment. As the base for collaboration activities moves from physical places to cyberspace, the methods of connecting every participant by means of a computer technology have been desired and considered. This study aims at investigating today's collaboration technology in the architectural design process, especially focused on the early stages, in terms of temporal dimensions. In addition, major concepts for and previous efforts and tools of collaborative design have been examined, and a specific recommendation has been proposed for future development of collaborative architectural design systems: That is distributed collaboration, which is accessible and comprehensible to all the professionals in the building design team, which not only allows the sharing of information but also the sharing of understanding, and which facilitates the development of design tools for different aspects of the envisioned collaborative design environment.

• International Journal of Advanced Culture Technology
• /
• v.8 no.4
• /
• pp.255-262
• /
• 2020

Recently, medical tourism is recognized as a high value-added industry because of its longer period of stay and higher expenditure than general tourism. In particular, although the number of medical tourists visiting Korea is increasing, the perception of Korean medical services is low. The purpose of this paper is to develop the app which, based on medical tourism type, recommends tourism destinations. Additionally, this proposed app can expand general tourism as well. It can provide tourists with medical information easily by sorting types tourists. Besides, as medical tourists normally stay long, we can take the advantage of post-treatment time. This app collects medical information data and tourist destination data, and categorizes the types of medical tourists into four categories: disease medical tourism, traditional medical tourism, cosmetic medical tourism, and recreational medical tourism. It provides medical information according to each type and recommends customized tourist destinations. User-based collaborative filtering is applied for tourist destination recommendations.

• Journal of information and communication convergence engineering
• /
• v.22 no.1
• /
• pp.7-13
• /
• 2024

User preferences and ratings may be anticipated by recommendation systems, which are widely used in social networking, online shopping, healthcare, and even energy efficiency. Constructing trustworthy recommender systems for various applications, requires the analysis and mining of vast quantities of user data, including demographics. This study focuses on holding elections with vague voter and candidate preferences. Collaborative user ratings are used by filtering algorithms to provide suggestions. To avoid information overload, consumers are directed towards items that they are more likely to prefer based on the profile data used by recommender systems. Better interactions between governments, residents, and businesses may result from studies on recommender systems that facilitate the use of e-government services. To broaden people's access to the democratic process, the concept of "e-democracy" applies new media technologies. This study provides a framework for an electronic voting advisory system that uses machine learning.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.7
• /
• pp.1083-1091
• /
• 2017

As big data technologies have been developed and massive data have exploded from users through various channels, CEO of global IT enterprise mentioned core importance of data in next generation business. Therefore various machine learning technologies have been necessary to apply data driven services but especially recommendation has been core technique in viewpoint of directly providing summarized information or exact choice of items to users in information flooding environment. Recently evolved recommendation techniques have been proposed by many researchers and most of service companies with big data tried to apply refined recommendation method on their online business. For example, Amazon used item to item collaborative filtering method on its sales distribution platform. In this paper, we develop a commercial web service for suggesting music contents and implement three representative collaborative filtering methods on the service. We also produce recommendation lists with three methods based on real world sample data and evaluate the usefulness of them by comparison among the produced result. This study is meaningful in terms of suggesting the right direction and practicality when companies and developers want to develop web services by applying big data based recommendation techniques in practical environment.

• Journal of the Korean Society of Industry Convergence
• /
• v.25 no.6_1
• /
• pp.953-959
• /
• 2022

In this paper, the design result of robotic tools and the development of robot control system for chemical coupler assembly are presented. This research aims to eliminate the risk of chemicals exposed to human operators by developing the robotic tools and robot automation system for chemical tank lorry unloading that were done manually. Due to tight tolerance between couplers, even small pose error may result in very large internal force. In order to resolve the problem, the 6-axis compliance device is employed, which can provide not only enough compliance between couplers but also F/T sensing. The 6-axis compliance device having large force and moment capacity is designed. A simple linear gripper with rack-and-pinion is designed to grasp two sizes of couplers. The proposed robot automation system consists of 6-DOF collaborative robot with offset wrist, 6-axis compliance device with F/T sensing, linear gripper, and two robot visions.

• Asian Journal of Innovation and Policy
• /
• v.5 no.3
• /
• pp.251-274
• /
• 2016

Practical methodology for categorizing collaborative disciplines or research in a quantitative manner is presented by developing a Correlation Matrix of Major Disciplines (CMMD) using bibliometric data collected between 2009 and 2014. First, 21 major disciplines in science and engineering are defined based on journal publication frequency. Second, major disciplines using a comparing discipline correlation matrix is created and correlation score using CMMD is calculated based on an analyzer function that is given to the matrix elements. Third, a correlation between the major disciplines and 14 research fields using CMMD is calculated for validation. Collaborative researches are classified into three groups by partially accepting the definition of pluri-discipline from peer review manual, European Science Foundation, inner-discipline, inter-discipline and cross-discipline. Applying simple categorization criteria identifies three groups of collaborative research and also those results can be visualized. Overall, the proposed methodology supports the categorization for each research field.

• Journal of information and communication convergence engineering
• /
• v.17 no.2
• /
• pp.135-141
• /
• 2019

Collaborative filtering algorithms often encounter data sparsity issues. To overcome this issue, auxiliary information of relevant items is analyzed and an item attribute matrix is derived. In this study, we combine the user-item attribute preference with the traditional similarity calculation method to develop an improved similarity calculation approach and use weights to control the importance of these two elements. A collaborative filtering algorithm based on user-item attribute preference is proposed. The experimental results show that the performance of the recommender system is the most optimal when the weight of traditional similarity is equal to that of user-item attribute preference similarity. Although the rating-matrix is sparse, better recommendation results can be obtained by adding a suitable proportion of user-item attribute preference similarity. Moreover, the mean absolute error of the proposed approach is less than that of two traditional collaborative filtering algorithms.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.357-359
• /
• 2023

This paper proposes a hybrid recommendation system (RS) model that overcomes the limitations of traditional approaches such as data sparsity, cold start, and scalability by combining collaborative filtering and context-aware techniques. The objective of this model is to enhance the accuracy of recommendations and provide personalized suggestions by leveraging the strengths of collaborative filtering and incorporating user context features to capture their preferences and behavior more effectively. The approach utilizes a novel method that combines contextual attributes with the original user-item rating matrix of CF-based algorithms. Furthermore, we integrate k-mean++ clustering to group users with similar preferences and finally recommend items that have highly rated by other users in the same cluster. The process of partitioning is the use of the rating matrix into clusters based on contextual information offers several advantages. First, it bypasses of the computations over the entire data, reducing runtime and improving scalability. Second, the partitioned clusters hold similar ratings, which can produce greater impacts on each other, leading to more accurate recommendations and providing flexibility in the clustering process. keywords: Context-aware Recommendation, Collaborative Filtering, Kmean++ Clustering.