• Korean Journal of Computational Design and Engineering
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• v.9 no.4
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• pp.325-333
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• 2004

To ensure competitiveness in the modern automotive market, material arrangements and information managements should be performed concurrently with new car developments. In automotive general assembly shops, thus, new business workflow and supporting environments are inevitable to reduce the manufacturing preparation time in developing a new car in the manner of concurrent and collaborative engineering. Since complete material arrangements for a whole general assembly system is a huge and complex job, several planners should execute their planning jobs and share information. Therefore, each planner should provide others with his/her results with continuous on-line communication and cooperation. Digital automotive general assembly factory is useful the performing concurrent and collaborative engineering and is essential for material arrangements and information managements systems. In this research, we constructed a sophisticated digital factory of an automotive general assembly shop by measuring and modeling through the parametric 3-D CAD, and a web-based system for concurrent and collaborative material arrangements for automotive general assembly via 3D mock-up is developed. By the digital general assembly shop and developed web-based system, savings in time and colt of manufacturing preparation activities are possible, and the reliability of the planning result Is greatly improved.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.803-811
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• 2010

This study analyzes the characteristics of preference ratings by dividing estimated values into four groups according to rank correlation coefficient after obtaining preference estimated value to user's ratings by using collaborative filtering algorithm. It is known that the value of standard error of skewness and standard error of kurtosis lower in the group of higher rank correlation coefficient This explains that the preference of higher rank correlation coefficient has lower extreme values and the differences of preference rating values. In addition, top n recommendation lists are made after obtaining rank fitting by using the result ranks of prediction value and the ranks of real rated values, and this top n is applied to the four groups. The value of top n recommendation is calculated higher in the group of higher rank correlation coefficient, and the recommendation accuracy in the group of higher rank correlation coefficient is higher than that in the group of lower rank correlation coefficient Thus, when using standard error of skewness and standard error of kurtosis in recommender system, rank correlation coefficient can be higher, and so the accuracy of recommendation prediction can be increased.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1643-1660
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• 2016

Connectivity and trust in social networks have been exploited to propose applications on top of these networks, including routing, Sybil defenses, and anonymous communication systems. In these networks, and for such applications, connectivity ensures good performance of applications while trust is assumed to always hold, so as collaboration and good behavior are always guaranteed. In this paper, we study the impact of differential behavior of users on performance in typical social network-enabled routing applications. We classify users into either collaborative or rational (probabilistically collaborative) and study the impact of this classification and the associated behavior of users on the performance of such applications, including random walk-based routing, shortest path based routing, breadth-first-search based routing, and Dijkstra routing. By experimenting with real-world social network traces, we make several interesting observations. First, we show that some of the existing social graphs have high routing costs, demonstrating poor structure that prevents their use in such applications. Second, we study the factors that make probabilistically collaborative nodes important for the performance of the routing protocol within the entire network and demonstrate that the importance of these nodes stems from their topological features rather than their percentage of all the nodes within the network.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.310-321
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• 2012

This study was conducted to establish collaborative governance for North Korean Refugees' settlement support service. Three major actors were identified. At the central government level, there is no control center to coordinate various functions. This study suggested three roles for central government in collborative governance, which are policy planning based on public value, allocating financial resources to implementing organizations, and program evaluation for securing public accountability. The rloes of local government are establishing communication channels between implementing participants, maintenancing good relations, and coordination. Finally, the role of private actors is developing high quality service programs, connecting local resources for settlement service.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.10 s.241
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• pp.1353-1360
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• 2005

Injection-molded products serve a wide range of applications in our modem lives and their significance is ever increasing. However, difficulty of communication among related companies under the present system results in increase of lead time and decrease of production efficiency. The objective of this paper is the development of a web-based draft verification system in mold design processes. Although several commercial CAD systems offer draft verification functions, those systems are very expensive and inadequate to perform collaborative works. For collaborative work under the distributed environment, the proposed system uses native file transforming of CAD data into optimal format by using the ACIS kernel and InterOp. Functions of draft verification modules are constructed over the ActiveX control using the visual C++ and OpenGL. Therefore, collaborators related to the development of a new product are able to verify the draft and undercut over the Internet without commercial CAD systems. The system helps to reduce production cost, errors and lead-time to the market. Performance of the system is confirmed through various case studies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.10C
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• pp.912-920
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• 2002

As computer systems and communication technologies develop rapidly, CSCW(Computer Supported Collaborative Work) system appears nowadays, through which it is available to work on virtual space without any restriction of time and place. Most of CWCS systems depend on a special network and groupware. The systems of graphics and CAD are not so many because they are specified by hardware and application software. We propose a Web-based collaborative CAD system which is independent from any platforms, and develop a 3D solid modeler in the system. This system can be worked in the environment of Client/Server architecture. Clients connect to the design server through Java applet on WWW. The server is implemented by Java application.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.11
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• pp.475-482
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• 2013

Several approaches to recommendation systems have been studied. One of the most successful technologies for building personalization and recommendation systems is collaborative filtering, which is a technique that provides a process of filtering customer information based on such information profiles. Collaborative filtering systems, however, have a sparsity if there is not enough data to recommend. In this paper, we suggest a movie recommendation system, based on the weighted personal propensity and the collaborating filtering system, in order to provide a solution to such sparsity. Furthermore, we assess the system's applicability by using the open database MovieLens, and present a weighted personal propensity framework for improvement in the performance of recommender systems. We successfully come up with a movie recommendation system through the optimal personalization factors.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.395-400
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• 2019

The CBS and the AEAS functionalities are defined in cellular systems and T-DMB systems, respectively. In the case that communication facilities are disabled in indoor environments, it is impossible for the residents to receive the emergency messages. In this paper, a novel collaborative technology of intelligent mobile robots is proposed, which relies on cooperative communications among the intelligent mobile robots. In order to improve the performance, the intelligent mobile robots exploit their location information. Simulation results confirm that the proposed method is very suitable for reliable emergency alert broadcast.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.1
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• pp.25-40
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• 2023

Recommendation systems are frequently used by users to generate intelligent automatic decisions. In the study of movie recommendation system, the existing approach uses largely collaboration and content-based filtering techniques. Collaborative filtering considers user similarity, while content-based filtering focuses on the activity of a single user. Also, mixed filtering approaches that combine collaborative filtering and content-based filtering are being used to compensate for each other's limitations. Recently, several AI-based similarity techniques have been used to find similarities between users to provide better recommendation services. This paper aims to provide the prospective expansion by deriving possible solutions through the analysis of various existing movie recommendation systems and their challenges.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.56-66
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• 2023

The goal of deep learning is to extract complex features from multidimensional data use the features to create models that connect input and output. Deep learning is a process of learning nonlinear features and functions from complex data, and the user data that is employed to train deep learning models has become the focus of privacy concerns. Companies that collect user's sensitive personal information, such as users' images and voices, own this data for indefinite period of times. Users cannot delete their personal information, and they cannot limit the purposes for which the data is used. The study has designed a deep learning method that employs privacy protection technology that uses distributed collaborative learning so that multiple participants can use neural network models collaboratively without sharing the input datasets. To prevent direct leaks of personal information, participants are not shown the training datasets during the model training process, unlike traditional deep learning so that the personal information in the data can be protected. The study used a method that can selectively share subsets via an optimization algorithm that is based on modified distributed stochastic gradient descent, and the result showed that it was possible to learn with improved learning accuracy while protecting personal information.