• Journal of Communications and Networks
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• 제14권1호
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• pp.15-20
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• 2012

As Internet protocol and wireless communications have developed, the number of different types of mobile services has increased gradually. Existing priority-computing schemes cannot satisfy the dynamic requirements of supporting multiple services in future wireless communication systems, because the currently used factors, mainly user priority, are relatively simple and lack relevancy. To solve this problem and provide the desired complexity, dynamic behavior, and fairness features of 3G and beyond 3G mobile communication systems, this paper proposes a novel cross-layer dynamic integrated priority-computing scheme that computes the priority based on a variety of factors, including quality of service requirements, subscriber call types, waiting time, movement mode, and traffic load from the corresponding layers. It is observed from simulation results that the proposed dynamic integrated priority scheme provides enhanced performance.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2008년도 춘계 종합학술대회 논문집
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• pp.547-550
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• 2008

최근에 블로그, UCC, IPTV 등 사용자 중심의 인터넷 서비스와 언제 어디서나 웹을 통해 서비스를 받을 수 있는 유비쿼터스 컴퓨팅 환경으로의 변화는 안정된 서비스를 제공할 수 있는 고가용 시스템 플랫폼을 필요로 한다. 고가용 시스템이란 네트워크상에 서버들을 클러스터로 구성함으로써 만약 서비스 하던 시스템이 고장과 같은 시스템 장애가 발생하더라도 계속해서 안전하게 서비스를 제공할 수 있는 시스템을 말한다. 그리고 이러한 고가용 시스템 플랫폼에서 서비스의 신뢰성을 위해 시스템 간 데이터 동기화는 필수적이다. 본 논문에서는 리눅스 고가용 시스템에서 로컬 디스크 간 실시간 데이터 동기화 기술인 DRBD(Distributed Replicated Block Disk)를 구현하였다.

• International journal of advanced smart convergence
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• 제8권3호
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• pp.115-122
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• 2019

Recent and ongoing discoveries of mycoviruses with new properties demand the development of an appropriate research infrastructure to analyze their evolution and classification. In particular, the discovery of negative-sense single-stranded mycoviruses is worth noting in genome types in which double-stranded RNA virus and positive-sense single-stranded RNA virus were predominant. In addition, some genomic properties of mycoviruses are more interesting because they have been reported to have similarities with the pathogenic virus family that infects humans and animals. Genetic information on mycoviruses continues to accumulate in public repositories; however, these databases have some difficulty reflecting the latest taxonomic information and obtaining specialized data for mycoviruses. Therefore, in this study, we developed a bioinformatics-based pipeline to efficiently utilize this genetic information. We also designed a schema for data processing and database construction and an algorithm to keep taxonomic information of mycoviruses up to date. The pipeline and database (termed 'mycoVDB') presented in this study are expected to serve as useful foundations for improving the accuracy and efficiency of future research on mycoviruses.

• 한국통신학회논문지
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• 제33권9B호
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• pp.802-812
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• 2008

본 논문에서는 초고속인터넷망에서 멀티미디어 서비스 제공 능력의 시험을 통하여 광대역통합망(BcN: Broadband convergence Network)에서의 서비스 제공 가능성 분석에 활용할 수 있는 초고속인터넷의 QoS 제공모델을 제시하고 품질수준을 검증하였다. 3개의 초고속인터넷 사업자가 제공 중인 $10{\sim}100Mbps$급 서비스를 대상으로 전국적으로 총 46개의 가입자를 선정하여 시험환경을 구축하고, BcN의 핵심서비스인 음성/영상전화, VoD 및 IPTV 서비스에 대한 품질시험을 수행하여 QoS 제공수준 및 품질저하 원인 등을 분석하였다. QoS 분석결과를 기초로 하여 BcN에서 활용이 가능한 고수준의 QoS 제공 모델을 제시하고, 광대역통합연구망(KOREN)에 QoS 시험망을 구축하여 QoS 적용으로 인한 서비스 품질확보 수준을 검증하였다. 연구결과는 국내외 통신 사업자들의 차세대통신망(NGN: Next Generation Network)기반 All-IP망의 구축에 중요한 자료로 활용될 것으로 기대한다.

• 한국지리정보학회지
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• 제9권2호
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• pp.30-41
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• 2006

이 논문은 효율적인 차량 이동 궤적 관리를 지원하는 e-logistics 시스템을 제안한다. 최근의 무선통신의 발전은 물류 차량의 추적, 휴대폰 사용자 위치 서비스, 위치기반 상거래를 포함한 위치기반서비스들이 등장하게 하였다. 물류 시스템은 물류 센터에서 차량과 배송품의 위치를 계속적으로 파악해야 하므로 차량 추적을 필수적으로 수반하게 된다. 좀 더 나아가서, 계속적으로 이동하는 차량과 배송품의 위치 궤적을 저장하고 관리하는 것은 효율적인 물류 계획과 배송을 지원하기 위해 필수 불가결한 요소이다. 제안된 시스템은 실세계 모바일 환경에서 물류 배송 정보의 효과적인 관리뿐만 아니라, 기존의 공간 데이터베이스가 갖는 공간객체 관리 기능을 포함한다. 또한 효과적인 물류 이동 경로 관리 및 검색을 수행하기 위하여 기존의 TB 트리의 데이터 갱신 성능을 개선하고, 다중버전 기법을 도입한 색인을 사용하였다. 제안된 시스템은 소포관제와 유사한 차량 추적 시스템, 위치기반서비스 등과 같이 실시간 모바일 환경에서 연속적으로 위치를 변경하는 이동객체 관련 분야에 적용 가능하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.751-770
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• 2019

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a state-of-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

• 한국인터넷방송통신학회논문지
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• 제22권5호
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• pp.69-73
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• 2022

본 논문에서는 LoG(Laplacian of Gaussian) 기반의 윤곽선 검출 기법을 통한 새로운 미세먼지 측정 방법을 제안한다. 미세먼지 측정을 위하여 CCTV 기반의 영상 이미지를 수집하고, RoI(Region of Interest)를 통해 이미지 범위를 지정한다. 지정된 영역에 GMM(Gaussian Mixture Model)을 적용하여 군집화 후, LoG 알고리즘을 통해 윤곽선을 검출하고 검출된 윤곽선 강도를 측정한다. 측정된 윤곽선의 강도 데이터를 기반으로 미세먼지의 농도를 결정한다. 본 논문에서 제안하는 알고리즘의 효용성을 입증하기 위하여 본교 연구실 주위에 설치된 CCTV 영상 이미지를 6~7월 한달간 수집하여 적용한 결과, 측정된 결과값은 미세먼지 농도와 범위를 계산하기에 충분함을 본 실험을 통해 입증하였다.

• Journal of Information Processing Systems
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• 제6권4호
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• pp.481-500
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• 2010

The Internet explosion and the increase in crucial web applications such as ebanking and e-commerce, make essential the need for network security tools. One of such tools is an Intrusion detection system which can be classified based on detection approachs as being signature-based or anomaly-based. Even though intrusion detection systems are well defined, their cooperation with each other to detect attacks needs to be addressed. Consequently, a new architecture that allows them to cooperate in detecting attacks is proposed. The architecture uses Software Agents to provide scalability and distributability. It works in two modes: learning and detection. During learning mode, it generates a profile for each individual system using a fuzzy data mining algorithm. During detection mode, each system uses the FuzzyJess to match network traffic against its profile. The architecture was tested against a standard data set produced by MIT's Lincoln Laboratory and the primary results show its efficiency and capability to detect attacks. Finally, two new methods, the memory-window and memoryless-window, were developed for extracting useful parameters from raw packets. The parameters are used as detection metrics.

• International journal of advanced smart convergence
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• 제10권1호
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• pp.1-11
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• 2021

Document page segmentation is an important step in building a quality optical character recognition module. The study examined already existing work on the topic of page segmentation and focused on the development of a segmentation model that has greater functional significance for application in an organization, as well as broad capabilities for managing the quality of the model. The main problems of document segmentation were highlighted, which include a complex background of intersecting objects. As classes for detection, not only classic text, table and figure were selected, but also additional types, such as signature, logo and table without borders (or with partially missing borders). This made it possible to pose a non-trivial task of detecting non-standard document elements. The authors compared existing neural network architectures for object detection based on published research data. The most suitable architecture was RetinaNet. To ensure the possibility of quality control of the model, a method based on neural network modeling using the RetinaNet architecture is proposed. During the study, several models were built, the quality of which was assessed on the test sample using the Mean average Precision metric. The best result among the constructed algorithms was shown by a model that includes four neural networks: the focus of the first neural network on detecting tables and tables without borders, the second - seals and signatures, the third - pictures and logos, and the fourth - text. As a result of the analysis, it was revealed that the approach based on four neural networks showed the best results in accordance with the objectives of the study on the test sample in the context of most classes of detection. The method proposed in the article can be used to recognize other objects. A promising direction in which the analysis can be continued is the segmentation of tables; the areas of the table that differ in function will act as classes: heading, cell with a name, cell with data, empty cell.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.135-155
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• 2024

Urban public health monitoring in smart cities focuses on the control of conditions and health challenges in urban environments. Considering the rapid spread of diseases and pandemics, it is important for health authorities to trace people carrying the virus. In smart cities, this tracing must be interoperable and intelligent, especially in indoor surfaces characterized by small distances between people. Therefore, to fight pandemics, it is necessary to start with the already-existing digital equipment of the Internet of Things, such as connected objects and smartphones. In this study, the developed system is employed to provide a social IoT network and suggest a strategy which allows reliable traceability without threatening the privacy of users. This IoT-based system allows respecting the social distance between persons sharing public services in smart cities without applying smartphone applications or severe confinement. It also permits a return to normal life in case of viral pandemic and ensures the much-desired balance between economy and health. The present study analyses previous proposed social distance systems then, unlike these studies, suggests an intelligent and distributed IoT based strategy for positioning students. Two scenarios of static and dynamic optimization-based placement of Bluetooth Low Energy devices are proposed and an experimental study shows the contribution and complementarity of the introduced contact tracing strategy with the applications on smartphones.