• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.67-72
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• 2023

This paper portrays the design and optimization of a wideband four element triangular dielectric resonator antenna (TDRA) using PSO. The proposed antenna's radiation characteristics were extracted using Ansoft HFSS software. At a resonant frequency of 5-7 GHz, the four element antenna provides nearly 21 percent bandwidth and the optimized gives 5.82 dBi peak gain. The radiation patterns symmetry and uniformity are maintained throughout the operating bandwidth. for WLAN (IEEE 802.16) and WiMAX applications, the proposed antenna exhibits a consistent symmetric monopole type radiation pattern with low cross polarisation. The proposed antenna's performance was compared to that of other dielectric resonator antenna (DRA) shapes, and it was discovered that the TDRA uses a lot less radiation area to provide better performance than other DRA shapes and PSO optimized antenna increases the gain of the antenna

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1300-1304
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• 2003

The following paper deals with a control problem of a mobile robot in home network environment. The home network causes the mobile robot to communicate with sensors to get the sensor measurements and to be adapted to the environment changes. To get the improved performance of control of a mobile robot in spite of the change in home network environment, we use the fuzzy inference system with multiple reward reinforcement learning. The multiple reward reinforcement learning enables the mobile robot to consider the multiple control objectives and adapt itself to the change in home network environment. Multiple reward fuzzy Q-learning method is proposed for the multiple reward reinforcement learning. Multiple Q-values are considered and max-min optimization is applied to get the improved fuzzy rule. To show the effectiveness of the proposed method, some simulation results are given, which are performed in home network environment, i.e., LAN, wireless LAN, etc.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.3-6
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• 2005

Mobile Network의 이동성에 대한 연구가 활발하게 진행되어 오면서, 이동네트워크의 복잡한 모델인 Nested Mobile Network에 대한 연구가 부각되고 있다. 이 Nested Mobile Network에 대한 연구에는 네트워크 내에 존재하는 노드와 외부의 CN(Correspondent Node)과의 패킷전송에서 경로최적화를 하기 위한 방법 등에 대한 연구가 진행되어 오고 있으며, Nested Mobile Network에서 Pinball routing 문제 등으로 인해 경로최적화가 이루어지지 못하고, 이 문제가 일반적인 Nested Mobile Network에서 패킷사이즈의 길이를 지나치게 길어지게 함으로써 전송지연을 발생시키는 것이다, 본 논문에서는 반복적인 CoA(Care-of-Address)의 치환과정을 통해 Nested Mobile Network 상의 Pinball routing 문제를 해결하기 위한 효율적인 방안을 제안하고자 한다.

• Transactions of the Korean Society of Automotive Engineers
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• v.15 no.5
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• pp.48-55
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• 2007

A bushing component of a vehicle suspension system is tested to capture the nonlinear behavior of rubber bushing element using the MTS 3-axes rubber test machine. The results of the tests are used to model the artificial neural network bushing model. The performances from the neural network model usually are dependent on the structure of the neural network. In this paper, maximum error, peak error, root mean square error, and error-to-signal ratio are employed to evaluate the performances of the neural network bushing model. A simple simulation is carried out to show the usefulness of the developed procedure.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.667-672
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• 2001

An optimization approach is used to partition the field of view. A cost function is defined to represent the constraints on the solution, which is then mapped onto a two-dimensional Hopfield neural network for minimization. Each neuron in the network represents a possible match between a field of view and one or multiple objects. Partition is achieved by initializing each neuron that represents a possible match and then allowing the network to settle down into a stable state. The network uses the initial inputs and the compatibility measures between a field of view and one or multiple objects to find a stable state.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.37-62
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• 2006

This paper considers the problem of designing logical tunnels in virtual private networks considering QoS guarantee which restricts the number of tunnel hops for each traffic routing. The previous researches focused on the design of logical tunnel itself and Steiner-tree based solution algorithms were proposed. However, we show that for some objective settings it is not sufficient and is necessary to consider both physical and logical connectivity at the same time. Thereupon, the concept of the layered network is applied to the logical tunnel design problem in virtual private networks. The layered network approach considers the design of logical tunnel as well as its physical routing and we propose a modified branch-and-price algorithm which is known to solve layered network design problems effectively. To show the performance of the proposed algorithm, computational experiments have been done and the results show that the proposed algorithm solves the given problem efficiently and effectively.

• Management Science and Financial Engineering
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• v.17 no.2
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• pp.39-62
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• 2011

In wireless sensor network, since each sensor is equipped with a limited power, efficient use of the energy is important. One possible network management scheme is to cluster the sensors into several sets, so that the sensors in each of the sets can completely perform the monitoring task. Then the sensors in one set become active to perform the monitoring task and the rest of the sensors switch to a sleep state to save energy. Therefore, we rotate the roles of the active set among the sensors to maximize the network lifetime. In this paper, we suggest an optimal algorithm for the maximum lifetime coverage problem which maximizes the network lifetime. For comparison, we implemented both the heuristic proposed earlier and our algorithm, and executed computational experiments. Our algorithm outperformed the heuristic concerning the obtained network lifetimes, and it found the solutions in a reasonable amount of time.

• Journal of Korea Port Economic Association
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• v.22 no.1
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• pp.175-198
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• 2006

This paper presents a network capacity model that can be used as an analytical tool for strategic planning and resource allocation for multimodal transportation systems. In the context of freight transportation, the multimodal network capacity problem (MNCP) is formulated as a mathematical model of nonlinear bi-level optimization problem. Given network configuration and freight demand for multiple origin-destination pairs, the MNCP model is designed to determine the maximum flow that the network can accommodate. To solve the MNCP, a heuristic solution algorithm is developed on the basis of a linear approximation method. A hypothetical exercise shows that the MNCP model and solution algorithm can be successfully implemented and applied to not only estimate the capacity of multimodal network, but also to identify the capacity gaps over all individual facilities in the network, including intermodal facilities. Transportation agencies and planners would benefit from the MNCP model in identifying investment priorities and thus developing sustainable transportation systems in a manner that considers all feasible modes as well as low-cost capacity improvements.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2414-2416
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• 2003

In this raper, we present a Wavelet Neural Network(WNN) approach to the solution of the tracking problem for mobile robots that possess complexity, nonlinearity and uncertainty. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome the problems caused by local minima of optimization and various uncertainties. This network structure is helpful to determine the number of the hidden nodes and the initial value of weights with compact structure. In our control method, the control signals are directly obtained by minimizing the difference between the reference track and the pose of a mobile robot that is controlled through a wavelet neural network. The control process is a dynamic on-line process that uses the wavelet neural network trained by the gradient-descent method. Through computer simulations, we demonstrate the effectiveness and feasibility of the proposed control method.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.194-198
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• 2023

The current electricity networks will undergo profound changes in the years to come to be able to meet the growing demand for electricity, while minimizing the costs of consumers and producers, etc. The electricity network of tomorrow or even the intelligent « Smart Grids » network will be the convergence of two networks: the electricity network and the telecommunications network. In this context falls our work which aims to study the impact of the integration of energy decentralization into the electricity network. In this sense, we have implemented a new smart grid model where several coexisting suppliers can exchange information with consumers in real time. In addition, a new approach to energy distribution optimization has been developed. The simulation results prove the effectiveness of this approach in improving energy exchange and minimizing consumer purchase costs and line losses.