• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.10
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• pp.5140-5147
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• 2013

This paper presents a new idea of structural design of the optical access network for IPOW(IP over WDM) services. More efficient network can be constructed, because the IP packets are transmitted directly to the WDM without going through an intermediate layer of networks. The wavelength Routing is based on a label switching technology. The ability to transmission of high volume traffics and QoS capability of the optical label switching directly to the end user of the IPOW optical internet networks is provided. As in AON(Active Optical Network) flexible bandwidth on demand subscribers is allocated. By the Simulation of the proposed optical access networks to measure the BER(Bits Error Ratio) at the end of the nodes the network characteristics are analyzed. These results are based on the design of efficient optical network.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.465-468
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• 2007

This paper addresses the scheme for transmitting Data and TDM signals based on E-PON. E-PON technology, that combines low-cost Ethernet technology and optical fiber infra-structure, has been appeared as a solution of next generation access network. The transmission speed of E-PON is 1Gbps and symmetric in both direction, such as downstream and upstream. Therefore, it is possible to save the cost through sample network architecture, efficient operation, and low maintenance cost of optical IP Ethernet network. By adding TBMoIP(Time Division Multiplexing over Internet Protocol) module to this E-PON system, and implementing QoS(Quality of Service) control function, this system can provide data and TDM service efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1518-1523
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• 2005

With the expansion of service over the internet, the recent network demands the amount of the more bandwidth and fast transfer rate. Optical Burst Switching has considered as a promising solution for supporting high-speed Internet Service. Because of OBS architecture, it has the security threats such as eavesdropping, masquerading, denial of service and so on. In this Paper, We analyze OBS-specific security threats and requirement for supporting security protocol n OBS networks. We propose an authentication and key exchange protocol for supporting the security service. This protocol supports explicit key authentication by using the control messages and protects the control message by using the session key.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.2
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4B
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• pp.455-463
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• 2004

In this paper, we deal with a ring-mesh network design problem arising from the deployment of WDM for the optical internet. The ring-mesh network consists of ring topology and full mesh topology for satisfying traffic demand while minimizing the cost of OAOMs and OXCs. The problem seeks to find an optimal clustering of traffic demands in the network such that the total number of node assignments is minimized, while satisfying ring capacity and node cardinality constraints. We formulate the problem as a mixed-integer programming model and prescribe a tabu search heuristic procedure Promising computational results within 3% optimality gap are obtained using the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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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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• v.9 no.8
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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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• v.6 no.9
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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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• v.14 no.10
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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.