• ETRI Journal
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• v.41 no.3
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• pp.348-357
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• 2019

Fog radio access network (F-RAN) architectures provide markedly improved performance compared to conventional approaches. In this paper, an efficient genetic algorithm-based content distribution scheme is proposed that improves the throughput and reduces the transmission delay of a F-RAN. First, an F-RAN system model is presented that includes a certain number of randomly distributed fog access points (F-APs) that cache popular content from cloud and other sources. Second, the problem of efficient content distribution in F-RANs is described. Third, the details of the proposed optimal genetic algorithm-based content distribution scheme are presented. Finally, simulation results are presented that show the performance of the proposed algorithm rapidly approaches the optimal throughput. When compared with the performance of existing random and exhaustive algorithms, that of the proposed method is demonstrably superior.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.479-484
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• 1998

As technology in a computer hardware and software advances, efficient information retrieval from multimedia database gets highly demanded. Recently, it has been actively exploited to retrieve information based on the stored contents. However, most of the methods emphasize on the points which are far from human intuition or emotion. In order to overcome this shortcoming , this paper attempts to apply interactive genetic algorithm to content-based image retrieval. A preliminary result with subjective test shows the usefulness of this approach.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.250-252
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• 2001

With the growth in computer and network technologies, some content-based music retrieval systems have been developed. However, their retrieval efficiency does not satisfy user's requirement yet. Of course users hope to have a more efficient and higher precision for music retrieval. In this paper so for these reasons, we Propose an efficient content-based music retrieval algorithm using melodies represented as music sequences. From the experimental result, it is shown that the proposed algorithm has higher exact rate than the related algorithms.

• Journal of the Semiconductor & Display Technology
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• v.7 no.4
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• pp.13-18
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• 2008

This paper explores a content-based image retrieval framework with relevance feedback based on genetic algorithm (GA). This framework adopts GA to learn the user preferences using the similarity functions defined for all available descriptors. The objective of the GA-based learning methods is to learn the user preferences using the similarity functions and to find a descriptor combination function that best represents the user perception. Experiments were performed to validate the proposed frameworks. The experiments employed the natural image databases and color and texture descriptors to represent the content of database images. The proposed frameworks were compared with the other two relevance feedback methods regarding effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.129-132
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• 2000

In this paper, we deal with content based searching algorithm for brand image using GHA(Generalized Hebbian Algorithm). GHA is a part of PCA(Principal Component Analysis), that has single-layer perceptron operates and self-organizing performances. We used this algorithm for feature extracts of brand images, and our simulations verify the high performance than present text based methods.

• Journal of Computing Science and Engineering
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• v.11 no.2
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• pp.39-48
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• 2017

Main content extraction of web pages is widely used in search engines, web content aggregation and mobile Internet browsing. However, a mass of irrelevant information such as advertisement, irrelevant navigation and trash information is included in web pages. Such irrelevant information reduces the efficiency of web content processing in content-based applications. The purpose of this paper is to propose an automatic main content extraction method of web pages. In this method, we use two indicators to describe characteristics of web pages: text density and hyperlink density. According to continuous distribution of similar content on a page, we use an estimation algorithm to judge if a node is a content node or a noisy node based on characteristics of the node and neighboring nodes. This algorithm enables us to filter advertisement nodes and irrelevant navigation. Experimental results on 10 news websites revealed that our algorithm could achieve a 96.34% average acceptable rate.

• Smart Structures and Systems
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• v.5 no.3
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• pp.209-221
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• 2009

A control algorithm for seismic protection of building structures based on the theory of variable structural control or sliding mode control is presented. The paper focus in the design of sliding surface. A method for determining the sliding surface by pole assignment algorithm where the poles of the system in the sliding surface are obtained on-line, based on the frequency content of the incoming earthquake signal applied to the structure, is proposed. The proposed algorithm consists of the following steps: (i) On-line FFT process is applied to the incoming part of the signal and its frequency content is recognized. (ii) A transformation of the frequency content to the complex plane is performed and the desired location of poles of the controlled structure on the sliding surface is estimated. (iii) Based on the estimated poles the sliding surface is obtained. (iv) Then, the control force which will drive the response trajectory into the estimated sliding surface and force it to stay there all the subsequent time is obtained using Lyapunov stability theory. The above steps are repeated continuously for the entire duration of the incoming earthquake. The potential applications and the effectiveness of the improved control algorithm are demonstrated by numerical examples. The simulation results indicate that the response of a structure is reduced significantly compared to the response of the uncontrolled structure, while the required control demand is achievable.

• Journal of the Semiconductor & Display Technology
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• v.10 no.3
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• pp.75-81
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• 2011

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1327-1331
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• 2010

Association rules are very useful and interesting patterns for discovering preferences of each person in digital-content services. The Apriori algorithm is an influential algorithm for mining frequent itemsets for association rules. However, since this algorithm does not take into account reference times of each content as an important support factor, it cannot be used to extract associations among time-based data. This paper proposes an augmented Apriori algorithm discovers association rules using both frequencies and usage times of each item.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.326-329
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• 2004

Content adaptation is an effective solution to support the quality of service over multimedia services over heterogeneous environments. This paper deals with the accuracy and the real-time requirement, two important issues in making decision on content adaptation. From our previous problem formulation, we propose an optimal algorithm and a fast approximation based on the Viterbi algorithm of dynamic programming. Through extensive experiments, we show that the proposed algorithms can enable accurate adaptation decisions, and especially they can support the real-time processing.