• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8B
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• pp.1150-1158
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• 2010

At present multimedia service composed of voice, data, and video service is prevalent in the Internet. As such, wide-scale penetration of Internet service imposes tremendous pressure to the network infrastructure such as the media servers and links as well as the nodes. In addition, users from a large-scale population require broad bandwidth and high level of QoS. This requires a network with reliable and scalable services to customers, which also necessitates a realistic method for the design of the a media server. In this work, we explore the influence of user's behavior about delay on the performance of media server that takes into account the system and user attributes in a realistic manner. By incorporating user's behavior about the delay-sensitivity, we present an analytic framework for the evaluation of the performance of the media server, via which we illustrate a meaningful intuition in the provision of Internet multimedia service.

• Journal of Information Processing Systems
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• v.10 no.4
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• pp.568-580
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• 2014

To enhance the utilization of the traffic channels of a network (instead of allocating radio channel to an individual user), a channel or a group of channels are allocated to a user group. The idea behind this is the statistical distribution of traffic arrival rates and the service time for an individual user or a group of users. In this paper, we derive the blocking probability and throughput of a subscriber station of Worldwide Interoperability for Microwave Access (WiMAX) by considering both the connection level and packet-level traffic under a complete partition scheme. The main contribution of the paper is to incorporate the traffic shaping scheme onto the incoming turbulent traffic. Hence, we have also analyzed the impact of the drain rate of the buffer on the blocking probability and throughput.

• Proceedings of the KOR-KST Conference
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• 2002.02a
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• pp.97-139
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• 2002

The aim of the present study is to find a good quality user equilibrium assignments under time varying condition. For this purpose, this study introduces a dynamic network loading method that can maintain correct flow propagation as well as flow conservation, and it develops a novel solution algorithm that does not need evaluation of the objective function by modifying the Schittenhelm (1990)'s algorithm. This novel algorithm turns out to be efficient and convenient compared to the conventional Frank-Wolfe (1956) algorithm because the former finds solutions based on routes rather than links so that it can maintain correct flow propagation intrinsically in the time-varying network conditions. The application of dynamic user equilibrium (DUE) assignment model with this novel solution algorithm to test networks including medium-sized one shows that the present DUE assignment model gives rise to high quality discrete time solutions when we adopt the deterministic queuing model for a link performance function, and we associate flows and costs in a proper way.

• Journal of the Korean Society for Library and Information Science
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• v.32 no.3
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• pp.265-280
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• 1998

The Current measures of library service quality do not adequately capture user's perceptions of service quality for libraries and information centers. The purposes of this study are (1) to develop a user-based concepts to measuring the quality of library service (2) to introduce a user-based service quality model and SERVQUAL instrument from service marketing areas and (3) to assess applicability of this model and instrument to libraries and information centers. The concluding section suggests several considerations for implementation of their applicability and future research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.9
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• pp.1742-1748
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• 2007

In order to provide the convenient access to the mobile services and the efficient utilization of the mobile devices, it is required to devise an intelligent user interface which guarantees the efficient task selection and transition in the limited input/output environments of the mobile devices. In this paper, we propose user task prediction models which are essential for the intelligent user interface, and an accuracy estimation model is also proposed for evaluating the prediction models.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.143-148
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• 2022

In this paper, we employ deep neural network (DNN) to discern Android user interaction data distribution from artificial data distribution. We utilize real Android user interaction trace dataset collected from [1] to evaluate our DNN design. In particular, we use sequential model with 4 dense hidden layers and 1 dense output layer in TensorFlow and Keras. We also deploy sigmoid activation function for a dense output layer with 1 neuron and ReLU activation function for each dense hidden layer with 32 neurons. Our evaluation shows that our DNN design fulfills high test accuracy of at least 0.9955 and low test loss of at most 0.0116 in all cases of artificial data distributions.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1242-1250
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• 2022

When recommending cold items that do not have user-item interactions to users, even we adopt state-of-the-arts algorithms, the predicted information of cold items tends to have lower accuracy compared to warm items which have enough user-item interactions. The lack of information makes for recommender systems to recommend monotonic items which have a few top popular contents matched to user preferences. As a result, under-diversified items have a negative impact on not only recommendation diversity but also on recommendation accuracy when recommending cold items. To address the problem, we adopt a diversification algorithm which tries to make distributions of accumulated contents embedding of the two items groups, recommended items and the items in the target user's already interacted items, similar. Evaluation on a real world data set CiteULike shows that the proposed method improves not only the diversity but also the accuracy of cold item recommendation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2903-2923
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• 2023

Knowledge recommendation is a type of recommendation system that recommends knowledge content to users in order to satisfy their needs. Although using graph neural networks to extract data features is an effective method for solving the recommendation problem, there is information loss when modeling real-world problems because an edge in a graph structure can only be associated with two nodes. Because one super-edge in the hypergraph structure can be connected with several nodes and the effectiveness of knowledge graph for knowledge expression, a dual-channel hypergraph convolutional neural network model (DCHC) based on hypergraph structure and knowledge graph is proposed. The model divides user data and knowledge data into user subhypergraph and knowledge subhypergraph, respectively, and extracts user data features by dual-channel hypergraph convolution and knowledge data features by combining with knowledge graph technology, and finally generates recommendation results based on the obtained user embedding and knowledge embedding. The performance of DCHC model is higher than the comparative model under AUC and F1 evaluation indicators, comparative experiments with the baseline also demonstrate the validity of DCHC model.

• Smart Media Journal
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• v.12 no.1
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• pp.62-76
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• 2023

The design of a museum mobile application's user experience demands an in-depth comprehension of the user experience dimensions and the unique attributes of the user experience elements that users may encounter. This paper through a series of experimental studies and analyses of experiences related to the use of museum mobile applications, derives a user experience framework consisting of four dimensions and 19 elements, which has been verified for feasibility. The study shows that all four dimensions have a positive impact on user satisfaction, but the degree of impact varies. Among them, emotionality of use and ease of use have the greatest impact, while usefulness of use and multiformity of function have relatively less impact. In addition, the user experience of museum mobile application can also positively influence the user's future intention to recommend, continue using, and even the actual visit to the museum.

• Informatization Policy
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• v.30 no.3
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• pp.92-121
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• 2023

As music streaming services (MSS) involve various interactions with users during the music consumption process, it is important to understand the user experience and manage the service accordingly. This study developed a user experience model for MSS by theoretically linking the quality characteristics considered important by music service users with the structure of user experience. PLS analysis was then performed using survey data to test the model. As a result, functionality (search, browsing, and personalized recommendation), UI usability, content quality (currentness, sufficiency, relevance), and monetary cost were found to be key experience factors that determine the experience consequence, i.e., user satisfaction. In addition, in a supplementary analysis comparing domestic and global services, differences in user experience were found between the two groups in terms of functionality and content quality. The user experience model of MSS proposed in this study serves as a new foundation for theory-based research in this field and provides meaningful implications for the competitive landscape among music streaming service platforms and for their competitive strategies.