• ETRI Journal
• /
• v.34 no.3
• /
• pp.352-360
• /
• 2012

This paper proposes a low-cost, low-power, and high-capacity optical-electrical-optical-type reach extender that can provide 3R frame regeneration and remote management to increase the reach and split ratio with no change to a legacy time division multiple access passive optical network. To provide remote management, the extender gathers information regarding optical transceivers and link status per port and then transmits to a service provider using a simple network management protocol agent. The extender can also apply to an Ethernet passive optical network (E-PON) or a gigabit-capable PON (G-PON) by remote control. In a G-PON, in particular, it can provide burst mode signal retiming and burst-to-continuous mode conversion at the upstream path through a G-PON transmission convergence frame adaptor. Our proposed reach extender is based on the quad-port architecture for cost-effective design and can accommodate both the physical reach of 60 km and the 512 split ratios in a G-PON and the physical reach of 80 km and the 256 split ratios in an E-PON.

• Journal of Korean Institute of Industrial Engineers
• /
• v.25 no.4
• /
• pp.500-509
• /
• 1999

In this paper, we consider an optimal routing problem when point-to-point and point-to-multipoint connection traffics are offered in an ATM network. We propose a mathematical model for cost-minimizing configuration of a logical network for a given ATM-based BISDN. Our model is essentially identical to the previous one proposed by Kim(Kim, 1996) which finds a virtual-path configuration where the relevant gains obtainable from the ATM technology such as the statistical multiplexing gain and the switching/control cost-saving gain are optimally traded-off. Unlike the Kim's model, however, ours explicitly considers the VP's QoS(Quality of Service) for more efficient utilization of bandwidth. The problem is a large-scale, nonlinear, and mixed-integer problem. The proposed algorithm is based on the local linearization of equivalent-capacity functions and the relaxation of link capacity constraints. As a result, the problem can be decomposed into moderate-sized shortest path problems, Steiner arborescence problems, and LPs. This fact renders our algorithm a lot faster than the previous nonlinear programming algorithm while the solution quality is maintained, hence application to large-scale network problems.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.28 no.1
• /
• pp.79-87
• /
• 2014

With the increase of demand for electricity power, new construction and expansion of transmission lines for transport have been required. However, it has been difficult to be realized by such opposition from environmental groups and residents. Therefore, the development of techniques for effective use of existing transmission lines is more needed. In this paper, the major variables to affect the allowable transmission capacity in an overhead transmission lines were selected and the dynamic line rating (DLR) method using artificial neural networks reflecting unique environment-heat properties was proposed. To prove the proposed method, the analyzed results using the artificial neural network were compared with the ones obtained from the existing method. The analyzed results using the proposed method showed an error of 0.9% within ${\pm}$, which was to be practicable.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.4
• /
• pp.627-635
• /
• 2008

Using directional antenna in wireless network can offer many advantages including significant decrease of interference, increase of spatial reuse and possibility of improving network capacity. However, existing 802.11 MAC is designed for use of omni-directional antenna then those advantages can not be shown in that MAC protocol when it uses directional antenna. In this paper, we present a MAC protocol specifically designed for directional antenna to achieve spatial reuse and improve capacity of MAC protocol. Simulation result shows the advantages of our proposal in comparison with existing MAC in terms of end-to-end delay and network throughput.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.22 no.5
• /
• pp.999-1007
• /
• 2012

Multi-precision multiplication over public key cryptography should be considered for performance enhancement due to its computational complexity. Particularly, embedded device is not suitable to execute high complex computation, public key cryptography, because of its limited computational power and capacity. To overcome this flaw, research on multi-precision multiplication with fast computation and small capacity is actively being conducted. In the paper, we explore the cutting-edge technology of multi-precision multiplication for efficient implementation of public key cryptography over sensor network. This survey report will be used for further research on implementation of public key cryptography over sensor network.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.11
• /
• pp.3892-3912
• /
• 2021

The recent rapid growth of mobile network traffic places multi-access edge computing in an important position to reduce network load and improve network capacity and service quality. Contrasting with traditional mobile cloud computing, multi-access edge computing includes a base station cooperative cache layer and user cooperative cache layer. Selecting the most appropriate cache content according to actual needs and determining the most appropriate location to optimize the cache performance have emerged as serious issues in multi-access edge computing that must be solved urgently. For this reason, a cache placement algorithm based on comprehensive utility in big data multi-access edge computing (CPBCU) is proposed in this work. Firstly, the cache value generated by cache placement is calculated using the cache capacity, data popularity, and node replacement rate. Secondly, the cache placement problem is then modeled according to the cache value, data object acquisition, and replacement cost. The cache placement model is then transformed into a combinatorial optimization problem and the cache objects are placed on the appropriate data nodes using tabu search algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, a multi-access edge computing experimental environment is built. Experimental results show that CPBCU provides a significant improvement in cache service rate, data response time, and replacement number compared with other cache placement algorithms.

• Computers and Concrete
• /
• v.28 no.6
• /
• pp.635-642
• /
• 2021

In this paper, soft learning techniques are used to predict the ultimate torsional capacity of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. Soft computing techniques, namely Artificial Neural Network, trained by various back propagation algorithms, and Particle Swarm Optimization (PSO) algorithm, have been used to model and predict the torsional strength of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. The performance of each model has been evaluated by using statistical parameters such as coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The hybrid PSO NN model resulted in an R² of 0.9292 with an RMSE of 5.35 for training and an R² of 0.9328 with an RMSE of 4.57 for testing. Another model, ANN BP, produced an R² of 0.9125 with an RMSE of 6.17 for training and an R² of 0.8951 with an RMSE of 5.79 for testing. The results of the PSO NN model were in close agreement with the experimental values. Thus, the PSO NN model can be used to predict the ultimate torsional capacity of RC beams strengthened with FRP with greater acceptable accuracy.

• Geomechanics and Engineering
• /
• v.38 no.4
• /
• pp.439-453
• /
• 2024

The research findings of two nonlinear machine learning and soft computing models- the Cuckoo optimization algorithm (COA) and the Teaching-learning-based optimization (TLBO) in combination with artificial neural network (ANN)-are presented in this article. Detailed finite element modeling (FEM) of a shallow footing on two layers of cohesionless soil provided the data sets. The models are trained and tested using the FEM outputs. Additionally, various statistical indices are used to compare and evaluate the predicted and calculated models, and the most precise model is then introduced. The most precise model is recommended to estimate the solution after the model assessment process. When the anticipated findings are compared to the FEM data, there is an excellent agreement, which indicates that the TLBO-MLP solutions in this research are reliable (R²=0.9816 for training and 0.99366 for testing). Additionally, the optimized COA-MLP network with a swarm size of 500 was observed to have R² and RMSE values of (0.9613 and 0.11459) and (0.98017 and 0.09717) for both the normalized training and testing datasets, respectively. Moreover, a straightforward formula for the soft computing model is provided, and an excellent consensus is attained, indicating a high level of dependability for the suggested model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.8
• /
• pp.3701-3727
• /
• 2016

Recently Service Function Chaining (SFC) is promising to innovate the network service mode in modern networks. However, a feasible implementation of SFC is still difficult due to the need to achieve functional equivalence with traditional modes without sacrificing performance or increasing network complexity. In this paper, we present a configurable network service chaining (CNSC) mechanism to provide services for network traffics in a flexible and optimal way. Firstly, we formulate the problem of network service chaining and design an effective service chain construction framework based on integrating software-defined networking (SDN) with network functions virtualization (NFV). Then, we model the service path computation problem as an integer liner optimization problem and propose an algorithm named SPCM to cooperatively combine service function instances with a network utility maximum policy. In the procedure of SPCM, we achieve the service node mapping by defining a service capacity matrix for substrate nodes, and work out the optimal link mapping policies with segment routing. Finally, the simulation results indicate that the average request acceptance ratio and resources utilization ratio can reach above 85% and 75% by our SPCM algorithm, respectively. Upon the prototype system, it is demonstrated that CNSC outperforms other approaches and can provide flexible and scalable network services.

• Journal of Information Processing Systems
• /
• v.13 no.6
• /
• pp.1544-1553
• /
• 2017

The network coding mechanism has attracted much attention because of its advantage of enhanced network throughput which is a desirable characteristic especially in a multi-hop wireless network with limited link capacity such as the device-to-device (D2D) communication network of 5G. COPE proposes to use the XOR-based network coding in the two-hop wireless network topology. For multi-hop wireless networks, the Distributed Coding-Aware Routing (DCAR) mechanism was proposed, in which the coding conditions for two flows intersecting at an intermediate node are defined and the routing metric to improve the coding opportunity by preferring those routes with longer queues is designed. Because the routes with longer queues may increase the delay, DCAR is inefficient in delivering real-time multimedia traffic flows. In this paper, we propose a network coding-aware routing protocol for multi-hop wireless networks that enhances DCAR by considering traffic load distribution and link quality. From this, we can achieve higher network throughput and lower end-to-end delay at the same time for the proper delivery of time-sensitive data flow. The Qualnet-based simulation results show that our proposed scheme outperforms DCAR in terms of throughput and delay.