• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.6
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• pp.721-729
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• 2016

CCN(Content-Centric Network) enables users to retrieve content using the content's name. Researchers face critical challenges in terms of mobility. Since the routing information is part of the content name, when the provider moves, it is necessary to update all the routers routing information. However, this requires significant costs. In this paper, we propose PNPCCN(Proactive Neighbor Pushing CCN), considering the popularity and rarity of mobility support, for providers in CCN environments. Via simulation studies, we demonstrate that our solutions are effective in terms of shorter numbers of retransmitted Interest packets, and average download times and higher delivery ratios during mobility.

• The Journal of the Korea Contents Association
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• v.6 no.5
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• pp.113-121
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• 2006

As Internet evolves, web sites are becoming more and more complex, and it costs a lot of time and money to update web sites. The explosive growth of a large amount of content has resulted in an excessive server load and high network traffic, and thus it decreases the quality of service. In this research, we designed and implemented an integrated content management system which can be constructed and managed the content easily, and which is combined a merchant system with a personal information system based on the distributed content gathering and searching in order to optimize the utilization of the content itself.

• ETRI Journal
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• v.38 no.6
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• pp.1190-1196
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• 2016

We propose a new bandpass filter (BPF)-based online channel normalization method to dynamically suppress channel distortion when the speech and channel noise components are unknown. In this method, an adaptive modulation frequency filter is used to perform channel normalization, whereas conventional modulation filtering methods apply the same filter form to each utterance. In this paper, we only normalize the two mel frequency cepstral coefficients (C0 and C1) with large dynamic ranges; the computational complexity is thus decreased, and channel normalization accuracy is improved. Additionally, to update the filter weights dynamically, we normalize the learning rates using the dimensional power of each frame. Our speech recognition experiments using the proposed BPF-based blind channel normalization method show that this approach effectively removes channel distortion and results in only a minor decline in accuracy when online channel normalization processing is used instead of batch processing

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.234-236
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• 2020

Content-adaptive training and transmission of the model parameters of neural networks can boost up the SR performance with higher restoration fidelity. In this case, efficient transmission of neural network parameters are essentially needed. Thus, we propose a novel method of compressing the network model parameters based on the training of network model parameters in the sense that the residues of filter parameters and content loss are jointly minimized. So, the residues of filter parameters are only transmitted to receiver sides for different temporal portions of video under consideration. This is advantage for image restoration applications with receivers (user terminals) of low complexity. In this case, the user terminals are assumed to have a limited computation and storage resource.

• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.77-86
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• 2013

In this paper, we propose a fast video retargeting method which preserves the contents of a video and converts the image size. Since the conventional Seam Carving which is the well-known content-aware image retargeting technique uses the dynamic programming method, the repetitive update procedure of the accumulation energy is absolutely needed to obtain seam. The energy update procedure cannot avoid the processing time delay because of many operations by the image full-searching. By applying the proposed method, frames which have similar features in video are classified into a scene, and the first frame of a scene is resized by the modified Seam Carving where multiple seams are extracted from candidate seams to reduce the repetitive update procedure. After resizing the first frame of a scene, all continuous frames of the same scene are resized with reference to the seam information stored in the previous frame without the calculation of the accumulation energy. Therefore, although the fast processing is possible with reducing complexity and without analyzing all frames of scene, the quality of an image can be analogously maintained with an existing method. The experimental results show that the proposed method can preserve the contents of an image and can be practically applied to retarget the image on real time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.78-87
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• 2015

Content-Centric Networking(CCN) is a new networking paradigm to search for the routing information needed to find a data from the content name, unlike conventional IP networks. In CCN, the mobility management, one of the CCN challenges, is consists of consumer mobility and content provider mobility. Among both, in the case of the content provider mobility, it requires too much overhead and time to update routing information on the corresponding routers. In this paper, we propose Popularity-based Pushing CCN(PoPCoN) which considers the content popularity to support effective mobility of content provider in CCN. Our proposed algorithm shortens content download time for the consumer and reduces the network overhead during mobility as compared to the existing approaches.

• ETRI Journal
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• v.37 no.2
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• pp.395-405
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• 2015

We address the problem of uncooperative person recognition through continuous monitoring. Multiple modalities, such as face, height, clothes color, and voice, can be used when attempting to recognize a person. In general, not all modalities are available for a given frame; furthermore, only some modalities will be useful as some frames in a video sequence are of a quality that is too low to be able to recognize a person. We propose a method that makes use of stochastic information updates of temporal modalities and environment estimators to improve person recognition performance. The environment estimators provide information on whether a given modality is reliable enough to be used in a particular instance; such indicators mean that we can easily identify and eliminate meaningless data, thus increasing the overall efficiency of the method. Our proposed method was tested using movie clips acquired under an unconstrained environment that included a wide variation of scale and rotation; illumination changes; uncontrolled distances from a camera to users (varying from 0.5 m to 5 m); and natural views of the human body with various types of noise. In this real and challenging scenario, our proposed method resulted in an outstanding performance.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.346-349
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• 2007

In this paper, the integrated search system made for the web news and review retrieval service is introduced. We made XSLTRobot that extract title, date, author and content from html document like news or reviews for search service. XSLTRobot used the XSLT technology in order to extract desired part of html page. The Intergrated Information Retrieval System(IIRS) is suitable for various search data format. And we introduce Dynamic Index Storage which is module of IIRS. Dynamic Index Storage is used to environment which needs fast index update like news. And it's design focused on retrieval performance because there was not many document that it has to update on a real time.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.531-536
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• 2002

In this paper, we have applied Genetic Algorithms(GAs) to Intrusion Detection System(TDS), and then proposed and simulated the misuse detection model firstly. We have implemented with the KBD contest data, and tried to simulated in the same environment. In the experiment, the set of record is regarded as a chromosome, and GAs are used to produce the intrusion patterns. That is, the intrusion rules are generated. We have concentrated on the simulation and analysis of classification among the Data Mining techniques and then the intrusion patterns are produced. The generated rules are represented by intrusion data and classified between abnormal and normal users. The different rules are generated separately from three models "Time Based Traffic Model", "Host Based Traffic Model", and "Content Model". The proposed system has generated the update and adaptive rules automatically and continuously on the misuse detection method which is difficult to update the rule generation. The generated rules are experimented on 430M test data and almost 94.3% of detection rate is shown.3% of detection rate is shown.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2A
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• pp.79-85
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• 2006

Generally, it is essential that high-speed routers, switches, and network security appliances should have an efficient packet classification scheme in order to achieve the high-speed packet forwarding capability. For the multi-gigabit packet-processing network equipment the high-speed content search hardware such as TCAM and search engine is recently used to support the content-based packet inspection. During the packet classification process, hundreds and thousands of rules are applied to provide the network security policies regarding traffic screening, traffic monitoring, and traffic shaping. In addition, these rules could be dynamically changed during operations of systems if anomaly traffic patterns would vary. Particularly, in the high-speed network, an efficient algorithm that updates and reorganizes the packet classification rules is critical so as not to degrade the performance of the network device. In this paper, we have proposed an efficient update algorithm using a partial-ordering that can relocate the dynamically changing rules at the TCAM. Experimental results should that our algorithm does not need to relocate existing rules feature until 70$\%$ of TCAM utilization.