• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.238-252
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• 2009

Due to the rapid increase of available contents via the convergence of broadcasting and internet, the efficient access to personally preferred contents has become an important issue. In this paper, for recommendation scheme for TV programs using a collaborative filtering technique is studied. For recommendation of user preferred TV programs, our proposed recommendation scheme consists of offline and online computation. About offline computation, we propose reasoning implicitly each user's preference in TV programs in terms of program contents, genres and channels, and propose clustering users based on each user's preferences in terms of genres and channels by dynamic fuzzy clustering method. After an active user logs in, to recommend TV programs to the user with high accuracy, the online computation includes pulling similar users to an active user by similarity measure based on the standard preference list of active user and filtering-out of the watched TV programs of the similar users, which do not exist in EPG and ranking of the remaining TV programs by proposed rank model. Especially, in this paper, the BM (Best Match) algorithm is extended to make the recommended TV programs be ranked by taking into account user's preferences. The experimental results show that the proposed scheme with the extended BM model yields 62.1% of prediction accuracy in top five recommendations for the TV watching history of 2,441 people.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.101-107
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• 2013

Is generated in real time in the real world, a large amount of time series data from a wide range of business areas. But it is not easy to determine the optimal model for the description and understanding of the time series data is represented as a dynamic feature. In this study, through the HMM suitable for estimating the short and long-term forecasting model of time-series data to estimate a model that can explain the characteristics of these time series data, it was estimated to predict future patterns of movement. The actual stock market through various materials, information criterion and optimal model estimation for the length of the most efficient data was found to accurately estimate the state of the model. Similar movement patterns predictive than the long-term prediction is more similar to the short-term prediction of the experimental result were found to be.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3502-3509
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• 2010

Streaming service in the wireless mobile network environment has been a very challenging issue due to the dynamic uncertain nature of the channels. Overhead such as congestion, latency, and jitter lead to the problem of performance degradation of an adaptive multi-streaming service. This paper proposes a AMSS (Adaptive Multi-level Streaming Service) mechanism to reduce the performance degradation due to overhead such as variable network bandwidth, mobility and limited resources of the wireless mobile network. The proposed AMSS optimizes streaming services by: 1) use of fuzzy similarity metric, 2) minimization of packet loss due to buffer overflow and resource waste, and 3) minimization of packet loss due to congestion and delay. The simulation result shows that the proposed method has better performance in congestion control and packet loss ratio than the other existing methods of TCP-based method, UDP-based method and VBM-based method. The proposed method showed improvement of 10% in congestion control ratio and 8% in packet loss ratio compared with VBM-based method which is one of the best method.

• KIISE Transactions on Computing Practices
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• v.23 no.8
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• pp.481-486
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• 2017

There are many scientists who want to perform experiments in a cloud environment, and pay-per-use services allow scientists to pay only for cloud services that they need. However, it is difficult for scientists to select a suitable set of resources since those resources are comprised of various characteristics. Therefore, classification is needed to support the effective utilization of cloud resources. Thus, a dynamic resource clustering method is needed to reflect the characteristics of the application that scientists want to execute. This paper proposes a resource clustering analysis method that takes into account the characteristics of an application in a hybrid cloud environment. The resource clustering analysis applies a Self-Organizing Map and K-means algorithm to dynamically cluster similar resources. The results of the experiment indicate that the proposed method can classify a similar resource cluster by reflecting the application characteristics.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.13-22
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• 2004

With the proliferation of multimedia data, there is an increasing need to support the indexing and retrieval of high-dimensional image data. Although there have been many efforts, the performance of existing multidimensional indexing methods is not satisfactory in high dimensions. Thus the dimensionality reduction and the approximate solution methods were tried to deal with the so-called dimensionality curse. But these methods are inevitably accompanied by the loss of precision of query results. Therefore, recently, the vector approximation-based methods such as the VA- file and the LPC-file were developed to preserve the precision of query results. However, the performance of the vector approximation-based methods depend largely on the size of the approximation file and they lose the advantages of the multidimensional indexing methods that prune much search space. In this paper, we propose a new index structure called the GC-tree for efficient similarity search in image databases. The GC-tree is based on a special subspace partitioning strategy which is optimized for clustered high-dimensional images. It adaptively partitions the data space based on a density function and dynamically constructs an index structure. The resultant index structure adapts well to the strongly clustered distribution of high-dimensional images.

• The KIPS Transactions:PartD
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• v.12D no.2 s.98
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• pp.219-232
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• 2005

With the rapid progress of computer technology in recent years, a digital video data has been used in many applications. As a result, various technologies have been introduced to discover knowledge embedded in a video database. However, few researches on data mining for a video database have been performed due to the unclear boundary of the information in a large amount of a video stream. This paper proposes an efficient generation method of a feature profile in a set of similar video data. To extract the information embedded in original video data efficiently, several refinement techniques are also presented. These methods include merging pixels, restricting preferred areas, removing noises under a minimum repeat factor, extracting important key frames, generating derived blocks and applying weights to dynamic and static areas differently. Finally, the performance of these methods is evaluated by comparing a result profile obtained by a data mining process with original video streams.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.632-640
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• 2017

Although solar insolation is the weather factor with the greatest influence on power generation in photovoltaic systems, the Meterological Agency does not provide solar insolation data for future dates. Therefore, it is essential to research prediction methods for solar insolation to efficiently manage photovoltaic systems. In this study, we propose a Dynamic Piecewise Prediction Model that can be used to predict solar insolation values for future dates based on information from the weather forecast. To improve the predictive accuracy, we dynamically divide the entire data set based on the sun altitude and cloudiness at the time of prediction. The Dynamic Piecewise Prediction Model is developed by applying a polynomial linear regression algorithm on the divided data set. To verify the performance of our proposed model, we compared our model to previous approaches. The result of the comparison shows that the proposed model is superior to previous approaches in that it produces a lower prediction error.

• Journal of the Korea Convergence Society
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• v.6 no.6
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• pp.185-193
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• 2015

The wisdom contents consists of an ordinary person's ideas and experience. The Wisdom Market [1] is an online business model where wisdom contents are traded. Thus, the general public could do business activities in the Wisdom Market at ease. As the wisdom contents are themselves the thought of persons, there exists many similar or duplicated contents. Existing copyright protection schemes mainly focus on the primary author's right. Thus, it's not appropriate for protecting the contents of Collective Intelligence that requires to protect the rights of collaborators. There should exist a new method to be dynamic capable of combining and deleting rights of select collaborators. In this study, we propose collective copyright authentication scheme suitable for the contents of Collective Intelligence. The proposed scheme consists of collective copyright registration, addition and verification protocols. It could be applied to various business models that require to combine multiple rights of similar contents or to represent multiple authorships on the same contents.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.93-96
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• 2008

The near fault ground motion(NFGM) is characterized by a single long period velocity pulse of large magnitude. NFGM's have been observed in recent strong earthquakes, Turkey Izmit (1999), Japan Kobe(1995), Northridge(1994), etc. These strong earthquakes have caused considerable damage to infrastructures because the epicenter was close to the urban area, called as NFGM. Extensive research for the far fault ground motion(FFGM) have been carried out in strong seismic region, but limited research have been done for NFGM in low or moderate seismic regions because of very few records. The purpose of this study is to investigate and analyze the effect of near-fault ground motions on RC bridge piers without lap-spliced longitudinal reinforcing steels. The seismic performance of two RC bridge piers under near-fault ground motions was investigated on the shake table. In addition, Two of four identical RC bridge piers were tested under a quasi-static load, and the others were under a pseudo-dynamic load. The respectively two RC bridge pier is comparatively subjected to Pseudo-dynamic loadings and Quasi-Static loadings. This paper indicated that more gives bigger ultimate strain of longitudinal steels to be fractured at bigger PGA motion.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.92-103
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• 2010

In this paper, a method for the recognition of dynamic hand gestures in various backgrounds using Dynamic Space Time Warping(DSTW) algorithm is proposed. The existing method using DSTW algorithm compares multiple candidate hand regions detected from every frame of the query sequence with the model sequences in terms of the time. However the existing method can not exactly recognize the models because a false path can be generated from the candidates including not-hand regions such as background, elbow, and so on. In order to solve this problem, in this paper, we use the invariant moments extracted from the candidate regions of hand and compare the similarity of invariant moments among candidate regions. The similarity is utilized as a weight and the corresponding value is applied to the matching cost between the model sequence and the query sequence. Experimental results have shown that the proposed method can recognize the dynamic hand gestures in the various backgrounds. Moreover, the recognition rate has been improved by 13%, compared with the existing method.