• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.1848-1849
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• 2006

The new robust controller design method is proposed for the flight control systems with model uncertainties. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides us with the ability to overcome the "explosion of complexity" problem of the backstepping controller. The SRWNNs are used to observe the arbitrary model uncertainties of flight systems and all their weights are trained on-line. From the Lyapunov stability analysis, their adaptation laws are induced and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a high performance aircraft (F-16) are utilized to validate the good tracking performance and robustness of the proposed control system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.202-204
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• 2005

This paper presents the formulation for developing 2 port Frequency Dependent AC System Equivalent(FDACSE) with the instantaneous term in 5-domain and illustrates its use. This 2 port FDNE have been applied to the New Zealand AC system. The electromagnetic transient package PSCAD/EMTDC is used to assess the transient response of the 2 port (FDACSE) developed with Norton Equivalent network. The study results have indicated the robustness and accuracy of 2 port FDACSE for electromagnetic transient studies.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.710-717
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• 1993

In this paper, we focus upon the design and applications of adaptive fuzzy-neuro controllers. An intelligent control system is proposed by exploiting the merits of two paradigms, a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to update the fuzzy control rules on-line with the output error. And, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권4호
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• pp.2196-2218
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• 2017

Multicast communication has been widely used in the Internet. However, multicast communication is vulnerable to DoS attack due to static router configuration. In this paper, HMC, a hopping multicast communication method based on SDN, is proposed to tackle this problem. HMC changes the multicast tree periodically and makes it difficult for the attackers to launch an accurate attack. It also decreases the probability of multicast communication being attacked by DoS and in the meanwhile, the QoS constrains are not violated. In this research, the routing problem of HMC is proven to be NP-complete and a heuristic algorithm is proposed to solve it. Experiments show that HMC has the ability to resist DoS attack on multicast route effectively. Theoretically, the multicast compromised probability can drop more than 0.6 when HMC is adopt. In addition, experiments demonstrate that HMC achieves shorter average multicast delay and better robustness compared with traditional method, and more importantly, it better defends DoS attack.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.29-34
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• 2001

This paper is devoted to investigating direct adaptive neural control of nonlinear systems with uncertain or unknown dynamic models. In the direct adaptive neural networks control area, theoretical issues of the existing backpropagation-based adaptive neural networks control schemes. The major contribution is proposing the variable index control approach, which is of great significance in the control field, and applying it to derive new stable robust adaptive neural network control schemes. This new schemes possess inherent robustness to system model uncertainty, which is not required to satisfy any matching condition. To demonstrate the feasibility of the proposed leaning algorithms and direct adaptive neural networks control schemes, intensive computer simulations were conducted based on the flexible joint robot systems and functions.

• Journal of Communications and Networks
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• 제11권6호
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• pp.564-573
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• 2009

In this paper, we consider a wireless multiple path network in which a transmitting node would like to send a message to the receiving node with a certain probability of success. These two nodes are separated by N erasure paths, and we devise two algorithms to determine minimum redundancy and optimal symbol allocation for this setup. We discuss the case with N = 3 and then extend the case to an arbitrary number of paths. One of the algorithms minimum redundancy algorithm in exponential time is shown to be optimal in several cases, but has exponential running time. The other algorithm, minimum redundancy algorithm in polynomial time, is sub-optimal but has polynomial worstcase running time. These algorithms are based off the theory of maximum-distance separable codes. We apply the MRAET algorithm on maximum-distance separable, Luby transform, and Raptor codes and compare their performance.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권12호
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• pp.518-525
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• 2006

This paper presents the adaptive robust control method for the flight control systems with model uncertainties. The proposed control system can be composed simply by a combination of the adaptive dynamic surface control (DSC) technique and the self recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides us with the ability to overcome the 'explosion of complexity' problem of the backstepping controller. The SRWNNs are used to observe the arbitrary model uncertainties of flight systems, and all their weights are trained on-line. From the Lyapunov stability analysis, their adaptation laws are induced and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a high performance aircraft (F-16) are utilized to validate the good tracking performance and robustness of the proposed control system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1035-1038
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• 1999

To improve the damping of all poorly damped oscillation modes, a control strategy of Static Synchronous Series Compensator (SSSC) based on energy method is presented in this Paper As a synchronous voltage-sourced inverter, SSSC is used to provide controllable series compensation. SSSC can provide controllable compensating voltage over an identical capacitive and inductive range. The damping effect of control strategy based on energy function is robustness with respect to loading condition, fault location and network configuration. Furthermore, the control inputs are based on local signals. In two area system, the effect of damping inter-area mode oscillation is demonstrated by the robust control strategy of SSSC.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.301-302
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• 2008

In this paper, we present an algorithm which decrease a frequency offset effect for 868MHz IEEE 802.15.4b LR - WPAN (Low Rate - Wireless Personal Area Network) receiver. The proposed method improve the robustness to frequency offset and receiver's stability using the multiple delay differential filter of receiver correlator.

• 전자공학회논문지C
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• 제36C권3호
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• pp.58-64
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• 1999

진화 프로그래밍은 두 가지 요소로 특징지을 수 있다. 하나는 선택 방법이고 나머지는 돌연변이 규칙이다. 본 논문에서는 신경회로망의 역전파 학습 알고리듬을 이용하여 돌연변이 연산을 수행하는 새로운 진화 프로그래밍 알고리듬을 제안한다. 신경회로망의 학습 알고리듬에서 현재 오차는 진화 프로그래밍의 개체가 진화해 나가야 할 방향을 지정해 주고, 관성은 개체의 변이에 지금까지의 진화 경향을 더해 주어서 빠르게 전역 최적해를 찾도록 하였다. 표준 테스트 함수를 이용하여 제안된 알고리듬의 성능과 강건함을 확인하였다.