• 한국경영과학회지
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• 제32권4호
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• pp.51-60
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• 2007

In this study, we consider the minimum ADM problem which is the fundamental problem for the cost-effective design of SONET ADM embedded in WDM ring networks. To minimize the number of SONET ADMs, efficient algorithms for the routing and wavelength assignment are needed. We propose a mathematical model based on the graph theory for the problem and propose a branch-and-price approach to solve the suggested model effectively within reasonable time. By exploiting the mathematical structure of ring networks, we developed polynomial time algorithms for column generation subroutine at branch-and-bound tree. In a computer simulation study, the suggested approach can find the optimal solution for sufficient size networks and shows better performance than the greedy heuristic method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2627-2629
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• 2003

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN). The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized networks, and to be much more flexible and preferable neural network than the conventional SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented with using nonlinear system data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권4호
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• pp.1679-1691
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• 2016

Successive interference cancellation (SIC) is an effective means of multi-packet reception to combat interference at the physical layer. We investigate the joint optimization issue of channel access and power control for capacity maximization in SIC-enabled wireless networks. We propose a new interference model to characterize the sequential detection nature of SIC. Afterward, we formulize the joint optimization problem, prove it to be a nondeterministic polynomial-time-hard problem, and propose a novel approximation approach based on the genetic algorithm (GA). Finally, we discuss the design and parameter setting of the GA approach and validate its performance through extensive simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권5호
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• pp.2375-2393
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• 2016

Key agreement is paramount in secure wireless communications. A promising approach to address key agreement schemes is to extract secure keys from channel characteristics. However, because channels lack randomness, it is difficult for wireless networks with stationary communicating terminals to generate robust keys. In this paper, we propose a Robust Secure Key Agreement (RSKA) scheme from Received Signal Strength (RSS) in stationary wireless networks. In order to mitigate the asymmetry in RSS measurements for communicating parties, the sender and receiver normalize RSS measurements and quantize them into q-bit sequences. They then reshape bit sequences into new l-bit sequences. These bit sequences work as key sources. Rather than extracting the key from the key sources directly, the sender randomly generates a bit sequence as a key and hides it in a promise. This is created from a polynomial constructed on the sender's key source and key. The receiver recovers the key by reconstructing a polynomial from its key source and the promise. Our analysis shows that the shared key generated by our proposed RSKA scheme has features of high randomness and a high bit rate compared to traditional RSS-based key agreement schemes.

• Journal of Communications and Networks
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• 제8권1호
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• pp.116-122
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• 2006

Most of the multimedia applications require strict quality of service (QoS) guarantee during the communication between a single source and multiple destinations. This gives rise to the need for an efficient QoS multicast routing strategy. Determination of such QoS-based optimal multicast routes basically leads to a multi-objective optimization problem, which is computationally intractable in polynomial time due to the uncertainty of resources in Internet. This paper describes a network model for researching the routing problem and proposes a new multicast tree selection algorithm based on genetic algorithms to simultaneously optimize multiple QoS parameters. The paper mainly presents a QoS multicast routing algorithm based on genetic algorithm (QMRGA). The QMRGA can also optimize the network resources such as bandwidth and delay, and can converge to the optimal or near-optimal solution within few iterations, even for the networks environment with uncertain parameters. The incremental rate of computational cost can close to polynomial and is less than exponential rate. The performance measures of the QMRGA are evaluated using simulations. The simulation results show that this approach has fast convergence speed and high reliability. It can meet the real-time requirement in multimedia communication networks.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2011년도 학술발표회
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• pp.473-473
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• 2011

Group method of data handling neural networks model (GMDH-NNM) is used to estimate daily reference evapotranspiration (ETo) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$), mean relative humidity ($RH_{mean}$) and sunshine duration (SD). And, for the performances of GMDH-NNM, it consists of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of GMDH-NNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily ETo data using GMDH-NNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as ETo modeling can be generalized using GMDH-NNM.

• Management Science and Financial Engineering
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• 제9권1호
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• pp.11-27
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• 2003

In this study, we propose a mathematical model based on the graph theory for the wavelength assignment problem arising in the design of SDH (Synchronous Digital Hierarchy) over WDM (Wavelength Division Multiplexing) ring networks. We propose a branch- and -price algorithm to solve the suggested models effectively within reasonable time in realistic SDH over WDM ring networks. By exploiting the structure of ring networks, we developed a polynomial time algorithm for efficient column generation and a branching rule that conserves the structure of column generation. In a computer simulation study, the suggested approach can find the optimal solutions within reasonable time and show better performance than the existing heuristics.

• International Journal of Control, Automation, and Systems
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• 제2권2호
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• pp.189-200
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• 2004

In this paper, we introduce a new soft computing technique that dwells on the ideas of combining fuzzy rules in a fuzzy system with polynomial neural networks (PNN). The PNN is a flexible neural architecture whose structure is developed through the modeling process. Unfortunately, the PNN has a fatal drawback in that it cannot be constructed for nonlinear systems with only a small amount of input variables. To overcome this limitation in the conventional PNN, we employed one of three principal soft computing components such as a fuzzy system. As such, a space of input variables is partitioned into several subspaces by the fuzzy system and these subspaces are utilized as new input variables to the PNN architecture. The proposed soft computing technique is achieved by merging the fuzzy system and the PNN into one unified framework. As a result, we can find a workable synergistic environment and the main characteristics of the two modeling techniques are harmonized. Thus, the proposed method alleviates the problems of PNN while providing superb performance. Identification results of the three-input nonlinear static function and nonlinear system with two inputs will be demonstrated to demonstrate the performance of the proposed approach.

• 정보처리학회논문지C
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• 제10C권5호
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• pp.635-640
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• 2003

정보통신 기술의 발전과 광범위한 통신망의 이용으로 인하여, 최근 생존성을 갖는 메쉬기반 네트워크의 설계는 주요 관심사가 되고 있다. 본 논문은 메쉬기반 광전송망에서의 예비용량 설계방안에 대해 논의한다. 본 연구에서는 메쉬기반 광전송망의 경로복구에 있어서, 예비용량 증가인자를 찾아 예비용량의 공유를 증가시키는 새로운 예비용량 설계방안을 제안한다. 정수계획법의 링크복구기법, SLPA, GA의 세 가지 다른 장애복구 방안과 더불어 제안한 방안의 성능을 비교한다. 이 방법은 예비 용량 할당을 결정하는데 있어서 휴리스틱 알고리즘과 결합하여 보다 우수한 성능을 보이고 있으며, 예비용량 할당을 결정하는 시간적인 면에서 대형 통신망으로 쉽게 확장할 수 있다. 이 새로운 방법의 주요 장점은 예비용량을 줄이고 다항식의 시간 복잡도를 갖는 것이다.

• 한국지능시스템학회논문지
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• 제20권1호
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• pp.101-108
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• 2010

본 논문에서는 퍼지 k-NN과 reconstruction error에 기반을 둔 feature selection을 이용한 lazy 분류기 설계를 제안하였다. Reconstruction error는 locally linear reconstruction의 평가 지수이다. 새로운 입력이 주어지면, 퍼지 k-NN은 local 분류기가 유효한 로컬 영역을 정의하고, 로컬 영역 안에 포함된 데이터 패턴에 하중 값을 할당한다. 로컬 영역과 하중 값을 정의한 우에, feature space의 차원을 감소시키기 위하여 feature selection이 수행된다. Reconstruction error 관점에서 우수한 성능을 가진 여러 개의 feature들이 선택 되어 지면, 다항식의 일종인 분류기가 하중 최소자승법에 의해 결정된다. 실험 결과는 기존의 분류기인 standard neural networks, support vector machine, linear discriminant analysis, and C4.5 trees와 비교 결과를 보인다.