• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2007년도 춘계학술대회 학술발표 논문집 제17권 제1호
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• pp.465-468
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• 2007

본 논문은 E-PON 시스템을 통하여 데이터 및 TDM 신호를 전달하기 위한 방안이다. 저가의 이더넷 기술과 광 인프라를 결합한 E-PON 기술은 차세대 액세스 네트워크의 솔루션으로 등장하였다. E-PON의 전송 속도는 1Gbps이며, 다운스트림과 업스트림인 양방향에 대하여 대칭적이다. 따라서 광 IP 이더넷 네트워크 간단한 네트워크 구조, 효율적인 운용, 그리고 낮은 유지비용을 통하여 비용을 상당히 절약할 수 있다. 이와 같은 E-PON 시스템에 TDMoIP(Time Division Multiplexing over Internet Protocol) 모듈을 첨가하고 QoS 제어 기능을 구현함으로써, 이 시스템은 데이터 및 TDM 서비스를 효율적으로 제공할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권4호
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• pp.1392-1403
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• 2015

The rapid development of both communication traffic and increasing optical network sizes has increased energy consumption. Traditional algorithms and strategies don't apply to controlling the expanded network. Immunization algorithms originated from the complex system theory are feasible for large-scale systems based on a scale-free network model. This paper proposes the immunization strategy for complex systems which includes random and targeted immunizations to solve energy consumption issues and uses traffic to judge the energy savings from the node immunization. The simulation results verify the effectiveness of the proposed strategy. Furthermore, this paper provides a possibility for saving energy with optical transmission networks.

• 한국정보통신학회논문지
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• 제9권7호
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• pp.1518-1523
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• 2005

인터넷 사용자의 증가에 따른 데이터 수요와 트래픽 증가에 따라 오늘날의 네트워크는 빠른 전송 속도와 넓은 대역폭을 요구한다. OBS 기반 광 네트워크는 이러한 요구사항을 만족시킬 수 있는 방안으로 활발히 연구가 진행되고 있으나, 도청, 위장, DoS 등의 보안 위협에 취약하다. 본 논문에서는 OBS 기반 광 네트워크에서 존재하는 보안 취약점 및 보안 공격을 분석하며, 이를 기반으로 안전한 서비스 제공을 위해 인증 및 키 분배 가능한 정보보호 프로토콜을 제시한다. 본 논문에서는 OBS 기반 광 네트워크에서 보안 기능을 강화하기 위해 제어 메시지를 이용하여 명시적 인증을 제공하며, 공통키값을 이용하여 제어 메시지를 보호한다.

• International Journal of Internet, Broadcasting and Communication
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• 제7권2호
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• pp.9-15
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• 2015

Various studies with regard to increasing the optical efficiency of plasmonic color filters have previously been conducted, such as mixing materials or applying diverse pattern shapes. Fundamentally, it is important to maximize the photonic crystal effect by finding the optimum periods of lattice as well as calculating the most efficient transmission area. In this study, we propose a technical method for optimizing the plasmonic color filters that have a high color gamut and luminance by analyzing the light spectrums based on the 1931 color coordinate system. Moreover, we suggest a calculation method in order to define the individual color purity of red and green and blue filters. Consequently, efficiency values are obtained independently from each color filter by evaluating the color purity and the luminance. The final result obtained from simulation are 27.6% of relative luminance and 25.3% of color gamut. The proposed optimization method is applicable to all plasmonic color filters having photonic crystal arrays.

• 한국통신학회논문지
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• 제29권4B호
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• pp.455-463
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• 2004

이 논문은 파장 분할 다중화 (Wavelength Division Multiplexing, WDM) 기술의 링-그물 구조를 가진 망을 설계하는 알고리즘을 제시한다. 링-그물 망을 설계하는 문제는 OADM과 OXC 비용을 최소로 하면서 트래픽을 만족시키는 그물 라우팅과 링 토폴로지를 설계한다. 링 토폴로지는 OADM으로 구성되어 링 내의 노드간 트래픽을 처리하고, 그물 라우팅은 서로 다른 링에 있는 노드간에 발생하는 트래픽을 OXC를 이용해서 처리한다. 링 토풀로지와 그물 라우팅 문제를 동시에 해결하기 위해서 정수 계획법 (Integer Programming) 모델을 개발한다. 링-그물 문제는 NP-Hard이므로 실제 크기의 망 문제에서 주어진 시간내 좋은해를 생성하는 효과적인 타부 서치 휴리스틱을 제안한다. 타부 서치 휴리스틱 성능을 상업용 소프트웨어인 CPLEX 7.0 으로 구한 해와 비교한 결과 5초 내에 총비용의 오차 범위가 3% 이내인 우수해를 구한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.4835-4855
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• 2018

In this paper, we study the sparse signal recovery that uses information of both support and amplitude of the sparse signal. A convergent iterative algorithm for sparse signal recovery is developed using Majorization-Minimization-based Non-convex Optimization (MM-NcO). Furthermore, it is shown that, typically, the sparse signals that are recovered using the proposed iterative algorithm are not globally optimal and the performance of the iterative algorithm depends on the initial point. Therefore, a modified MM-NcO-based iterative algorithm is developed that uses prior information of both support and amplitude of the sparse signal to enhance recovery performance. Finally, the modified MM-NcO-based iterative algorithm is used to estimate the time-varying sparse wireless channels with temporal correlation. The numerical results show that the new algorithm performs better than related algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.2948-2963
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• 2015

In the field of computer vision, visual surveillance systems have recently become an important research topic. Growth in this area is being driven by both the increase in the availability of inexpensive computing devices and image sensors as well as the general inefficiency of manual surveillance and monitoring. In particular, the ultimate goal for many visual surveillance systems is to provide automatic activity recognition for events at a given site. A higher level of understanding of these activities requires certain lower-level computer vision tasks to be performed. So in this paper, we propose an intelligent activity recognition model that uses a structure learning method and a classification method. The structure learning method is provided as a K2-learning algorithm that generates Bayesian networks of causal relationships between sensors for a given activity. The statistical characteristics of the sensor values and the topological characteristics of the generated graphs are learned for each activity, and then a neural network is designed to classify the current activity according to the features extracted from the multiple sensor values that have been collected. Finally, the proposed method is implemented and tested by using PETS2013 benchmark data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권9호
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• pp.2170-2190
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• 2012

There are nowadays strong demands for intelligent surveillance systems, which can infer or understand more complex behavior. The application of crowd density estimation methods could lead to a better understanding of crowd behavior, improved design of the built environment, and increased pedestrian safety. In this paper, we propose a new crowd density estimation method, which aims at estimating not only a moving crowd, but also a stationary crowd, using images captured from surveillance cameras situated in various public locations. The crowd density of the moving people is measured, based on the moving area during a specified time period. The moving area is defined as the area where the magnitude of the accumulated optical flow exceeds a predefined threshold. In contrast, the stationary crowd density is estimated from the coarseness of textures, under the assumption that each person can be regarded as a textural unit. A multilayer neural network is designed, to classify crowd density levels into 5 classes. Finally, the proposed method is experimented with PETS 2009 and the platform of Gangnam subway station image sequences.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.4117-4135
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• 2020

Due to the angle of view, illumination and scene diversity, real-time dynamic face detection and recognition is no small difficulty in those unrestricted environments. In this study, we used the intrinsic correlation between detection and calibration, using a multi-task cascaded convolutional neural network(MTCNN) to improve the efficiency of face recognition, and the output of each core network is mapped in parallel to a compact Euclidean space, where distance represents the similarity of facial features, so that the target face can be identified as quickly as possible, without waiting for all network iteration calculations to complete the recognition results. And after the angle of the target face and the illumination change, the correlation between the recognition results can be well obtained. In the actual application scenario, we use a multi-camera real-time monitoring system to perform face matching and recognition using successive frames acquired from different angles. The effectiveness of the method was verified by several real-time monitoring experiments, and good results were obtained.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권4호
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• pp.1519-1534
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• 2016

In this paper, we study how to optimize the data shuffle phase by leveraging the flow relationship in datacenter networks (DCNs). In most of the clustering computer frameworks, the completion of a transfer (a group of flows that can enable a computation stage to start or complete) is determined by the flow completing last, so that limiting the rate of other flows (not the last one) appropriately can save bandwidth without impacting the performance of any transfer. Furthermore, for the flows enter network late, more bandwidth can be assigned to them to accelerate the completion of the entire transfer. Based on these characteristics, we propose the flow chasing algorithm (FCA) to optimize the completion of the entire transfer. We implement FCA on a real testbed. By evaluation, we find that FCA can not only reduce the completion time of data transfer by 6.24% on average, but also accelerate the completion of data shuffle phase and entire job.