• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.74-81
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• 1997

This paper proposes an evolutionary learning algorithm to discipline the projection neural nctworks (PNNs) with special type of hidden nodes which can activate radial basis functions as well as sigmoid functions. The proposed algorithm not only trains the parameters and the connection weights hut also c~ptimizes the network structure. Through the structure optimization, the number of hidden node:; necessary to represent a given target function is determined and the role of each hidden node is decided whether it activates a radial basis function or a sigmoid function. To apply the algorithm, PNN is realized by a self-organizing genotype representation with a linked list data structure. Simulations show that the algorithm can build the PNN with less hidden nodes than thc existing learning algorithm using error hack propagation(EE3P) and network growing strategy.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.2062-2069
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.182-188
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• 2001

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.1-19
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• 2020

In an underlay cognitive simultaneous wireless information and power transfer (SWIPT) network, communication from secondary user (SU) to secondary destination (SD) is accomplished with decode-and-forward (DF) relays. Multiple energy-constrained relays are assumed to harvest energy from SU via power splitting (PS) protocol and complete SU secure information transmission with beamforming. Hence, physical layer security (PLS) is investigated in cognitive SWIPT network. In order to interfere with eavesdropper and improve relay's energy efficiency, a destination-assisted jamming scheme is proposed. Namely, SD transmits artificial noise (AN) to interfere with eavesdropping, while jamming signal can also provide harvested energy to relays. Beamforming vector and power splitting ratio are jointly optimized with the objective of SU secrecy capacity maximization. We solve this non-convex optimization problem via a general two-stage procedure. Firstly, we obtain the optimal beamforming vector through semi-definite relaxation (SDR) method with a fixed power splitting ratio. Secondly, the best power splitting ratio can be obtained by one-dimensional search. We provide simulation results to verify the proposed solution. Simulation results show that the scheme achieves the maximum SD secrecy rate with appropriate selection of power splitting ratio, and the proposed scheme guarantees security in cognitive SWIPT networks.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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• pp.623-628
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• 2000

Video-on-demand system in which users can request any video through the network at any time is made possible by rapid increase in network bandwidth and capacity of the media server. However true video-on-demand system cannot support all requests since bandwidth requirement is still too demanding. Therefore efficient bandwidth reduction algorithm is necessary. both the piggybacking method and the batching method are novel solutions that can provide more logical number of streams than the physical system can support. Of course each of them has its pros and cons. hence piggybacking with batching-by-size can take advantage of both the schemes. Some parameters such as the size of batch and the size of the catch-up window should be adjusted and order to maximize the bandwidth reduction for piggybacking with batching-by-size method. One of the most important parameters is decided optimally in this paper. Simulation shows that the optimized parameter can achieve considerable reductionand consequently remarkable enhancement in performance.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1952-1953
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• 2011

본 논문에서는 다항식 기반 Radial Basis Function(RBF)신경회로망(Polynomial based Radial Basis function Neural Network)을 설계하고 이를 n-클래스 패턴 분류 문제에 적용한다. 제안된 다항식기반 RBF 신경회로망은 입력층, 은닉층, 출력층으로 이루어진다. 입력층은 입력 벡터의 값들을 은닉층으로 전달하는 기능을 수행하고 은닉층과 출력층사이의 연결가중치는 상수, 선형식 또는 이차식으로 이루어지며 경사 하강법에 의해 학습된다. Networks의 최종 출력은 연결가중치와 은닉층 출력의 곱에 의해 퍼지추론의 결과로서 얻어진다. 패턴분류기의 최적화는 PSO(Particle Swarm Optimization)알고리즘을 통해 이루어진다. 그리고 제안된 패턴분류기는 실제 얼굴인식 시스템으로 응용하여 직접 CCD 카메라로부터 입력받은 데이터를 영상 보정, 얼굴 검출, 특징 추출 등과 같은 처리 과정을 포함하여 서로 다른 등록인물의 n-클래스 분류 문제에 적용 및 평가되어 분류기로써의 성능을 분석해본다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.794-799
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• 2015

To manage cell outage problem in indoor wireless communication systems, we should resolve the problem of abrupt network failure quickly. In this paper, we propose a near-optimal cell outage management (NoCOM) scheme to support seamless services to users. In consideration of system throughput, user fairness, and the guarantee of QoS simultaneously, the NoCOM scheme finds the solution of subchannel and power allocations using a non-convex optimization technique and allocates radio resources to users iteratively. Through intensive simulations, we verify the outstanding performances of the proposed NoCOM scheme with respect to the average cell capacity, user fairness, and computational complexity.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.6
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• pp.506-513
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• 2001

The torque of an SRM depends on the phase current and derivative of inductance. But an SRM is difficult to control the desired torques because of saturation in magnetic circuit An SRM is controlled by parameters of input voltage, and switch on , off angle The switch on off angles of an SRM regulate the magnitude and shape of current waveform and decide the magnitude and shape of torque This paper proposes an the optimization control scheme by adjusting both the switch on an switch off angle . The switch off angles are decided by reference of efficiency using simulation and experiments. The switch on angles are decided by load torque , And the dwell angles are controlled for torque control and speed control using GA-neural network which is used to simulated the reasonable switching angle.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1309-1314
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• 2007

The assignment of cells to reporting or non-reporting cells is an NP-hard problem having an exponential complexity in the Reporting Cell Location Management (RCLM) system. Frequent location update may result in degradation of quality of service due to interference. Miss on the location of a mobile terminal will necessitate a search operation on the network when a call comes in. The number of reporting cells and which cell must be reporting cell should be determined to balance the registration (location update) and search (paging) operations to minimize the cost of RCLM system. T1is paper compares Max-Min ant system (MMAS), rank-based ant system (RAS) and hybrid method of MMAS and RAS that generally used to solve combinatorial optimization problems. Experimental results demonstrate that hybrid method of MMAS and RAS is an effective and competitive approach in fairly satisfactory results with respect to solution quality and execution time for the optimal design of location management system.

• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.203-207
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• 2007

NEMO 기본 지원 (NEMO-BS, NEMO Basic Support) 프로토콜에서 MNN(Mobile Network Node)가 CN(Correspondent Node) 과 통신을 하기 위해서는 항상 MR(Mobile Router)과 HA(Home Agent) 사이의 양방향 터널을 이용해야 한다. 그러나 NEMO-BS 방식은 노드 간 데이터 전송 지연과 부분 구간에 대한 공격 가능성이 존재한다. 따라서 본 논문에서는 NEMO를 위한 인증된 경로 최적화(ATRO) 프로토콜을 제안한다. MR은 홈 링크로부터 멀어졌다고 판단되면 MNN으로부터 위임 권한을 얻기 위해 권한 위임 프로토콜을 수행한다. 그런 후에 MR과 CN은 공개키 암호 방식을 이용하여 자신의 의탁주소(CoA, Care-of Address)를 MNN의 홈 주소(HoA, Home-of Address)와 매핑하기 위한 등록 과정을 수행한다. 이때 각 노드의 주소 소유권 증명을 위해 암호학적으로 생성한 주소(CGA, Cryptographically Generated Address)를 이용한다. 성능분석에서는 구간별 안전성과 종단간 패킷 전송 지연 시간을 통해 프로토콜을 분석한다.