• Journal of Broadcast Engineering
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• v.20 no.2
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• pp.310-339
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• 2015

In this paper, the possibility of a terrestrial fixed 4K UHD (Ultra High Definition) and mobile HD (High Definition) convergence broadcasting service through a single channel employing the FEF (Future Extension Frame) multiplexing technique in DVB (Digital Video Broadcasting)-T2 (Second Generation Terrestrial) systems is examined. The performance of such a service is also investigated. FEF multiplexing technology can be used to adjust the FFT (fast Fourier transform) and CP (cyclic prefix) size for each layer, whereas M-PLP (Multiple-Physical Layer Pipe) multiplexing technology in DVB-T2 systems cannot. The convergence broadcasting service scenario, which can provide fixed 4K UHD and mobile HD broadcasting through a single terrestrial channel, is described, and transmission requirements of the SHVC (Scalable High Efficiency Video Coding) technique are predicted. A convergence broadcasting transmission system structure is described by employing FEF and transmission technologies in DVB-T2 systems. Optimized transmission parameters are drawn to transmit 4K UHD and HD convergence broadcasting by employing a convergence broadcasting transmission structure, and the reception performance of the optimized transmission parameters under AWGN (additive white Gaussian noise), static Brazil-D, and time-varying TU (Typical Urban)-6 channels is examined using computer simulations to find the TOV (threshold of visibility). From the results, for the 6 and 8 MHz bandwidths, reliable reception of both fixed 4K UHD and mobile HD layer data can be achieved under a static fixed and very fast fading multipath channel.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.