• Journal of Communications and Networks
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• v.14 no.6
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• pp.710-718
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• 2012

The implementation of a common control channel is one of the most challenging issues in cognitive radio networks, since a fully reliable control channel cannot be created without reserving bandwidth specifically for this purpose. In this paper, we investigate a promising solution that exploits the ultra wide band (UWB) technology to let cognitive radio nodes discover each other and exchange control information for establishing a communication link. The contribution of this paper is threefold: (i) We define the communication protocol needed to let cognitive radio nodes discover each other and exchange control information for link set up, (ii) we overcome the gap in coverage, which typically exists between UWB and long-medium range technologies, by using multi-hop communication, (iii) we evaluate the performance of our approach by adopting an accurate channel model and show its benefits with respect to an in-band signaling solution.

• Journal of Communications and Networks
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• v.6 no.4
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• pp.317-330
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• 2004

In this paper, we explore the utility of recently discovered multiple-antenna techniques (namely MIMO techniques) for medium access control (MAC) design and routing in mobile ad hoc networks. Specifically, we focus on ad hoc networks where the spatial diversity technique is used to combat fading and achieve robustness in the presence of user mobility. We first examine the impact of spatial diversity on the MAC design, and devise a MIMO MAC protocol accordingly. We then develop analytical methods to characterize the corresponding saturation throughput for MIMO multi-hop networks. Building on the throughout analysis, we study the impact of MIMO MAC on routing. We characterize the optimal hop distance that minimizes the end-to-end delay in a large network. For completeness, we also study MAC design using directional antennas for the case where the channel has a strong line of sight (LOS) component. Our results show that the spatial diversity technique and the directional antenna technique can enhance the performance of mobile ad hoc networks significantly.

• Journal of Communications and Networks
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• v.9 no.3
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• pp.236-246
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• 2007

This paper investigates the performance of the transmission control protocol (TCP) transport protocol over IEEE 802.11 infrastructure based wireless networks. A wireless link is generally characterized by high transmission errors, random interference and a varying latency. The erratic packet losses usually lead to a curbing of the flow of segments on the TCP connection and thus limit TCP's performance. This paper examines the impact of the lossy nature of IEEE 802.11 wireless networks on the TCP performance and proposes a scheme to improve the performance of TCP over wireless links. A negative acknowledgment scheme, selective negative acknowledgment (SNACK), is applied on TCP over wireless networks and a series of ns-2 simulations are performed to compare its performance against that of other TCP schemes. The simulation results confirm that SNACK and its proposed enhancement SNACK-S, which incorporates a bandwidth estimation model at the sender, outperform conventional TCP implementations in 802.11 wireless networks.

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.105-116
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• 2019

Recently, there has been an increase in research on wireless sensor networks (WSNs) because they are easy to deploy in applications such as internet-of-things (IoT) and body area networks. However, WSNs have constraints in terms of power, quality-of-service (QoS), computation, and others. To overcome the power constraint issues, wireless energy harvesting has been introduced into WSNs, the application of which has been the focus of many studies. Additionally, to improve system performance in terms of achievable rate, cooperative networks are also being explored in WSNs. We present a review on current research in the area of energy harvesting in WSNs, specifically on the application of simultaneous wireless information and power transfer (SWIPT) in a cooperative sensor network. In addition, we discuss possible future extensions of SWIPT and cooperative networks in WSNs.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.11
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• pp.151-162
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• 1994

A new neural network structure called Self-organized Distributed Networks (SODN) is proposed for developing the neural network-based multidimensional system models. The learning with the proposed networks is fast and precise. Such properties are caused from the local learning mechanism. The structure of the networks is combination of dual networks such as self-organized networks and multilayered local networks. Each local networks learns only data in a sub-region. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the proposed networks. The simulation results of the proposed networks show better performance than the standard multilayer neural networks and the Radial Basis function(RBF) networks.

• Journal of Korea Multimedia Society
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• v.15 no.12
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• pp.1456-1463
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• 2012

In this paper, we study the ON/OFF control policy of base stations in two-tier heterogeneous cellular networks to minimize the total power consumption of the system. Using heterogeneous cellular networks is a potential approach of providing higher throughput and coverage compared to conventional networks with only macrocell deployment, but in fact heterogeneous cellular networks often operates regardless of total power consumption, which is a very important issue of modern cellular networks. We propose a policy that controls the activation/deactivation of base stations in heterogeneous cellular networks to minimize total power consumption. Under this policy, the total power consumed can be significantly reduced when the traffic is low while the QoS requirement is satisfied.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.422-428
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• 2007

This paper presents a neural network based adaptive control approach to a reconfigurable flight control law that keeps handling qualities in the presence of faults or failures to the control surfaces of an aircraft. This approach removes the need for system identification for control reallocation after a failure and the need for an accurate aerodynamic database for flight control design, thereby reducing the cost and time required to develope a reconfigurable flight controller. Neural networks address the problem caused by uncertainties in modeling an aircraft and pseudo control hedging deals with the nonlinearity in actuators and the reconfiguration of a flight controller. The effect of the reconfigurable flight control law is illustrated in results of a nonlinear simulation of an unmanned aerial vehicle Durumi-II.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.53 no.4
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• pp.195-200
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• 2004

Many kinds of speed sensorless control system of induction motor had been developed. But it is difficult to implement at the real system because of complex algorithm and equations. This paper investigates a novel speed sensorless control of induction motor using neural networks. The proposed control strategy is based on neural networks using stator current and output of neural model based on state observer. The errors between the stator current and the output of neural model are back-propagated to adjust the rotor speed, so that adaptive state variable will coincide with the desired state variable. This algorithm may overcome several shortages of conventional model, such as integrator problems, small EMF at low speed and relatively large sensitivity of stator resistance variation. Also, this paper presents a newly developed optimal equation about the momentum constant and the learning rate. The proposed algorithms are verified through simulation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.11
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• pp.476-482
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• 2006

Based on a field-oriented model of induction motor, an adaptive backstepping control approach using neural networks is proposed in this paper for the speed control of induction motors with uncertainties at a minimum of information. Neural networks are used to approximate most of uncertainties which are derived from unknown motor parameters, load torque disturbances and unknown nonlinearities and an adaptive backstepping controller is used to derive adaptive law of neural networks and control input directly. The controller is implemented by the hardware using DSP and the effectiveness of the proposed approach is verified by carrying out the experiment.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.467-467
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• 2012

As the features of recent flood are spatially concentrated, loss of life and property increase by the impact of climate change. In addition to this the public interest in water control information is increased and socially reasonable justification of water control policy is needed. It is necessary to estimate the flood risk in order to let people know the status of flood control and establish flood control policy. For accurate flood risk analysis, we should consider inter-relation between causal factors of flood damage. Hence, flood risk analysis should be applied to interdependence of the factors selected. The Bayesian networks are ideally suited to assist decision-making in situations where there is uncertainty in the data and where the variables are highly interlinked. In this research, to provide more proper water control information the flood risk analysis is performed using the Bayesian networks to handle uncertainty and dependency among 13 specific proxy variables.