• Journal of Electrical Engineering and Technology
• /
• v.13 no.4
• /
• pp.1740-1751
• /
• 2018

This paper proposes a novel fault tolerant tracking control (FTTC) scheme for a class of nonlinear systems with actuator failures based on the policy iteration (PI) algorithm and the adaptive fault observer. The estimated actuator failure from an adaptive fault observer is utilized to construct an improved performance index function that reflects the failure, regulation and control simultaneously. With the help of the proper performance index function, the FTTC problem can be transformed into an optimal control problem. The fault tolerant tracking controller is composed of the desired controller and the approximated optimal feedback one. The desired controller is developed to maintain the desired tracking performance at the steady-state, and the approximated optimal feedback controller is designed to stabilize the tracking error dynamics in an optimal manner. By establishing a critic neural network, the PI algorithm is utilized to solve the Hamilton-Jacobi-Bellman equation, and then the approximated optimal feedback controller can be derived. Based on Lyapunov technique, the uniform ultimate boundedness of the closed-loop system is proven. The proposed FTTC scheme is applied to reconfigurable manipulators with two degree of freedoms in order to test the effectiveness via numerical simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.05a
• /
• pp.306-309
• /
• 2009

MANET(Mobile Ad-hoc Network)은 기간망에 의존하지 않는 이동 노드들로 구성된 자율망 또는 추론망 토폴로지에 의한 멀티홉 무선 네트워크이다. MANET을 구성하는 각 노드의 이동성, 속도 그리고 에너지와 같은 다양한 속성정보는 망의 특징과 운영을 결정하는 요인이다. 특히 망의 운영상, 전송 대역폭과 에너지 사용에 따른 제약을 가지며 이러한 특징을 고려한 라우팅 프로토콜의 설계 및 하드웨어 개발이 중요하게 요구된다. 본 논문에서는 계층적 클러스터 구조의 MANET 환경에서 노드의 에너지 속성과 네트워크의 트래픽 상태를 고려한 적응적 시간차 노드관리 기법인 ATICC(Adaptive Time Interval Clustering Control)을 제안한다. 제안된 ATICC은 시간차 노드 관리기법인 TICC(Time Interval Clustering Control)[1]에 기반하며 노드에 최적화된 Active/Sleep, Idle Listening 상태를 적응적으로 설정한 후 패킷을 전송함으로서 계층적 클러스터 내의 각 노드의 균형적인 에너지 소모를 이루는 에너지 효율적인 방식이다. 제안한 노드관리 방법은 기존의 LEACH, TICC과 비교 실험하고 그 성능을 검증하였다. 실험 결과, 제안한 노드관리 방법이 노드별 에너지 소모량을 줄였으며 전체 네트워크의 생존시간을 연장함으로서 기존의 방법 보다 우수함을 확인하였다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.30 no.12B
• /
• pp.811-817
• /
• 2005

Efficient bandwidth management is necessary in order to provide high quality service to users in a multimedia wireless/mobile network. In this paper, I propose an on-line load balancing algorithm with preemption. This technique is able to balance the traffic load among cells accommodating heterogeneous multimedia services while ensuring efficient bandwidth utilization. The most important features of my algorithm are its adaptability, flexibility and responsiveness to current network conditions. In addition, my online scheme to control bandwidth adaptively is a cell-oriented approach. This approach has low complexity making it practical for real cellular networks. Simulation results indicate the superior performance of my algorithm.

• Journal of Communications and Networks
• /
• v.10 no.1
• /
• pp.38-43
• /
• 2008

In this paper, adaptive channel borrowing approach fuzzy neural networks for load balancing (ACB-FNN) is presented to maximized the number of served calls and the depending on asymmetries traffic load problem. In a wireless network, the call's arrival rate, the call duration and the communication overhead between the base station and the mobile switch center are vague and uncertain. A new load balancing algorithm with cell involved negotiation is also presented in this paper. The ACB-FNN exhibits better learning abilities, optimization abilities, robustness, and fault-tolerant capability thus yielding better performance compared with other algorithms. It aims to efficiently satisfy their diverse quality-of-service (QoS) requirements. The results show that our algorithm has lower blocking rate, lower dropping rate, less update overhead, and shorter channel acquisition delay than previous methods.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2001.01a
• /
• pp.133-141
• /
• 2001

Since the computing environment changes very rapidly, the estimation of software effort is very difficult because it is not easy to collect a sufficient number of relevant cases from the historical data. If we pinpoint the cases, the number of cases becomes too small. However is we adopt too many cases, the relevance declines. So in this paper we attempt to balance the number of cases and relevance. Since many researches on software effort estimation showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the basic estimator. We propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set. eliminating the qualitative input factors with the same values can reduce the scale of the neural network model. Since there exists a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case, set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the Case-Set Selection Algorithm. This algorithm can be adopted in the case-adaptive software effort estimation systems.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.48 no.6
• /
• pp.140-148
• /
• 2011

Multimedia streaming service is susceptible to loss and delay of data as it requires high bandwidth and real time processing. Therefore QoS cannot be guaranteed due to data loss caused by heavy network traffic. To deal with these problems, a study on algorithm that can provide high quality multimedia service by considering both network congestion and characteristics of multimedia data is required. In this paper, we propose APQM algorithm which probabilistically removes packet by the congestion level of the queue in wireless station. The comparison with other existing scheduling algorithms shows tht congestion in the network is reduced and multimedia service quality of the proposed algorithm is improved.

• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.325-327
• /
• 2005

In this paper, we propose the design of active queue management (AQM) control system using the self-recurrent wavelet neural network (SRWNN). By regulating the queue length close to reference value, AQM can control the congestions in TCP network. The SRWNN is designed to perform as a feedback controller for TCP dynamics. The parameters of network are tunes to minimize the difference between the queue length of TCP dynamic model and the output of SRWNN using gradient-descent method. We evaluate the performances of the proposed AQM approach through computer simulations.

• Journal of information and communication convergence engineering
• /
• v.7 no.2
• /
• pp.141-147
• /
• 2009

To initialize and maintain self-organizing networks such as mobile ad hoc networks, address allocation protocol is essentially required. However, centralized approaches that pervasively used in traditional networks are not recommended in this kind of networks since they cannot handle with mobility efficiently. In addition, previous distributed approaches suffer from inefficiency with control overhead caused by duplicated address detection and management of available address pool. In this paper, we propose a new dynamic address allocation scheme, which is based on adaptive partition. An available address is managed in distributed way by multiple agents and partitioned adaptively according to current network environments. Finally, simulation results reveal that a proposed scheme is superior to previous approach in term of address acquisition delay under diverse simulation scenarios.

• Journal of Advanced Navigation Technology
• /
• v.13 no.2
• /
• pp.201-207
• /
• 2009

Recently effective spectrum resource technologies have been studied using a game theorectical approach for cognitive radio networks. Radio resource management is required an effective scheme because the performance of a radio communication system much depends on it's effectiveness. In this paper, we suggest a game theoretical algorithm for adaptive power control which is required an effect scheme in cognitive radio networks. It will be a distributed network. In the network distributed cognitive radio secondary users require an adaptive power control. There are many results which are suggested some possibility of game theoretical approaches for communication resource sharing. However, we suggest a practical game algorithm to achieve Nash equilibrium of all secondary users using a Nash equilibrium theorem in this paper. Particularly, a game model was analyzed for adaptive power control of a cognitive radio network, which is involved in DSSS (Direct Sequence Spread Spectrum) techniques. In case of K=63 and N=12 in the DSSS network, the number of iteration was less than maximum 200 using the suggested algorithm.

• Journal of Communications and Networks
• /
• v.12 no.1
• /
• pp.74-85
• /
• 2010

For the purpose of compromising hosts, attackers including infected hosts initially perform a portscan using IP addresses in order to find vulnerable hosts. Considerable research related to portscan detection has been done and many algorithms have been proposed and implemented in the network intrusion detection system (NIDS). In order to distinguish portscanners from remote hosts, most portscan detection algorithms use a fixed threshold that is manually managed by the network manager. Because the threshold is a constant, even though the network environment or the characteristics of traffic can change, many false positives and false negatives are generated by NIDS. This reduces the efficiency of NIDS and imposes a high processing burden on a network management system (NMS). In this paper, in order to address this problem, we propose an automatic portscan detection system using an fast increase slow decrease (FISD) scheme, that will automatically and adaptively set the threshold based on statistical data for traffic during prior time periods. In particular, we focus on reducing false positives rather than false negatives, while the threshold is adaptively set within a range between minimum and maximum values. We also propose a new portscan detection algorithm, rate of increase in the number of failed connection request (RINF), which is much more suitable for our system and shows better performance than other existing algorithms. In terms of the implementation, we compare our scheme with other two simple threshold estimation methods for an adaptive threshold setting scheme. Also, we compare our detection algorithm with other three existing approaches for portscan detection using a real traffic trace. In summary, we show that FISD results in less false positives than other schemes and RINF can fast and accurately detect portscanners. We also show that the proposed system, including our scheme and algorithm, provides good performance in terms of the rate of false positives.