• 한국통신학회논문지
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• 제16권11호
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• pp.1167-1178
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• 1991

전투지역 환경의 특징을 조사하고 요구되는 네트워크 성질을 고려하여 그 성질에 적합한 네트워크 제어구조를 설계한다. 또한 네트워크의 제어구조를 구성하며 제어구조에서 형성된 근간 네트워크에서의 전송계획을 작성하기 위한 분산 제어 알고리즘을 설계한다. 전투지역에서 변화하는 네트워크의 연결성 유사와 노드의 파괴로 인한 네트워크의 즉각적인 재구성을 신뢰성 있게 하기 위한 분산 알고리즘과 또한 공통 채널을 이용하는 네트워크에서 노드간에 전송충돌이 없는 전송 계획은 작성하기 위한 분산 알고리즘을 제안한다. 특히 중요한 노드가 파괴 및 사라질 때 되도록 적은 지연을 통해 네트워크가 동작할 수 있는 방법을 제안한다. 그리고 제안된 분신 알고리즘을 시뮬레이션을 통해 검증하고 네트워크 동작에 관해 설명한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.5972-5989
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• 2019

With the onset of the big data age, data is growing exponentially, and the issue of how to optimize large-scale data processing is especially significant. Large-scale global optimization (LSGO) is a research topic with great interest in academia and industry. Spark is a popular cloud computing framework that can cluster large-scale data, and it can effectively support the functions of iterative calculation through resilient distributed datasets (RDD). In this paper, we propose a hybrid mechanism of particle swarm optimization (PSO) and differential evolution (DE) algorithms based on Spark (SparkPSODE). The SparkPSODE algorithm is a parallel algorithm, in which the RDD and island models are employed. The island model is used to divide the global population into several subpopulations, which are applied to reduce the computational time by corresponding to RDD's partitions. To preserve population diversity and avoid premature convergence, the evolutionary strategy of DE is integrated into SparkPSODE. Finally, SparkPSODE is conducted on a set of benchmark problems on LSGO and show that, in comparison with several algorithms, the proposed SparkPSODE algorithm obtains better optimization performance through experimental results.

• Journal of Communications and Networks
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• 제18권2호
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• pp.150-161
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• 2016

Coordinated multi-point transmission (CoMP) techniques are being touted as enabling technologies for interference mitigation in next generation heterogeneous wireless networks (HetNets). In this paper, we present a comparative performance study of uplink (UL) CoMP algorithms for the 3GPP LTE HetNets. Focusing on a distributed and functionally-split architecture, we consider six distinct UL-CoMP algorithms: 1. Joint reception in the frequency-domain (JRFD) 2. Two-stage equalization (TSEQ) 3. Log-likelihood ratio exchange (LLR-E) 4. Symmetric TSEQ (S-TSEQ) 5. Transport block selection diversity (TBSD) 6. Coordinated scheduling with adaptive interference mitigation (CS-AIM) where JRFD, TSEQ, S-TSEQ, TBSD and CS-AIM are our main contributions in this paper, and quantify their relative performances via the post-processing signal-to-interference-plus-noise ratio distributions.We also compare the CoMP-specific front-haul rate requirements for all the schemes considered in this paper. Our results indicate that, with a linear minimum mean-square error receiver, the JRFD and TSEQ have identical performances, whereas S-TSEQ relaxes the front-haul latency requirements while approaching the performance of TSEQ. Furthermore, in a HetNet environment, we find that CS-AIM provides an attractive alternative to TBSD and LLR-E with a significantly reduced CoMP-specific front-haul rate requirement.

• 품질경영학회지
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• 제48권4호
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• pp.553-566
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• 2020

Purpose: The purpose of this study was to design a framework for generating one-class classification algorithm based on Hyper-Rectangle(H-RTGL) in a distributed environment connected by network. Methods: At first, we devised one-class classifier based on H-RTGL which can be performed by distributed computing nodes considering model and data parallelism. Then, we also designed facilitating components for execution of distributed processing. In the end, we validate both effectiveness and efficiency of the classifier obtained from the proposed framework by a numerical experiment using data set obtained from UCI machine learning repository. Results: We designed distributed processing framework capable of one-class classification based on H-RTGL in distributed environment consisting of physically separated computing nodes. It includes components for implementation of model and data parallelism, which enables distributed generation of classifier. From a numerical experiment, we could observe that there was no significant change of classification performance assessed by statistical test and elapsed time was reduced due to application of distributed processing in dataset with considerable size. Conclusion: Based on such result, we can conclude that application of distributed processing for generating classifier can preserve classification performance and it can improve the efficiency of classification algorithms. In addition, we suggested an idea for future research directions of this paper as well as limitation of our work.

• International Journal of Control, Automation, and Systems
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• 제4권2호
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• pp.165-171
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• 2006

This paper proposes a data fusion algorithm of nonlinear multi sensor dynamic systems of synchronous sampling based on filtering step by step. Firstly, the object state variable at the next time index can be predicted by the previous global information with the systems, then the predicted estimation can be updated in turn by use of the extended Kalman filter when all of the observations aiming at the target state variable arrive. Finally a fusion estimation of the object state variable is obtained based on the system global information. Synchronously, we formulate the new algorithm and compare its performances with those of the traditional nonlinear centralized and distributed data fusion algorithms by the indexes that include the computational complexity, data communicational burden, time delay and estimation accuracy, etc.. These compared results indicate that the performance from the new algorithm is superior to the performances from the two traditional nonlinear data fusion algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2278-2282
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• 2003

In this paper the structure of 2-DOF controller based on artificial immune network algorithms has been suggested for nonlinear system. Up to present time, a number of structures of the 2-DOF controllers are considered as 2-DOF (2-Degrees Of Freedom) control functions. However, a general view is provided that they are the special cases of either the state feedback or the modification of PID controllers. On the other hand, the immune network system possesses a self organizing and distributed memory, also it has an adaptive function by feed back law to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation, since antibody recognizes specific antigens which are the foreign substances that invade living creatures. Therefore, it can provide optimal solution to external environment. Simulation results by immune based 2-DOF controller reveal that immune algorithm is an effective approach to search for 2-DOF controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1531-1536
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• 2003

In this paper the structure of 2-DOF controller based on artificial immune network algorithms has been suggested for nonlinear system. Up to present time, a number of structures of the 2-DOF controllers are considered as 2-DOF (2-Degrees Of Freedom) control functions. However, A general view is provided that they are the special cases of either the state feedback or the modification of PID controllers. On the other hand, The immune network system possesses a self organizing and distributed memory, also it has an adaptive function by feed back law to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation, since antibody recognizes specific antigens which are the foreign substances that invade living creatures. Therefore, it can provide optimal solution to external environment. Simulation results by immune based 2-DOF controller reveal that immune algorithm is an effective approach to search for 2-DOF controller.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.229-232
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• 2001

Recently, several promising multiobjective evolutionary algorithms, e,g, SPEA, NSGA-ll, PESA, and SPEA2, have been developed. In this paper, we also propose a new multiobjective evolutionary algorithm that compares to them. In the algorithm proposed in this paper, we introduce a novel concept, "inherited age" and total algorithm is executed based on the inherited age concept. Also, we propose a new sharing algorithm, called objective classication sharing algorithm(OCSA) that can preserve the diversity of the population. We will show the superior performance of the proposed algorithm by comparing the proposed algorithm with other promising algorithms for the test functions.

• ETRI Journal
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• 제34권5호
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• pp.771-774
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• 2012

Typical channel allocation algorithms for secondary users do not include processes to reduce the frequency of switching from one channel to another caused by random interruptions by primary users, which results in high packet drops and delays. In this letter, with the purpose of decreasing the number of switches made between channels, we propose a nonparametric channel allocation algorithm that uses robust kernel density estimation to effectively schedule idle channel resources. Experiment and simulation results demonstrate that the proposed algorithm outperforms both random and parametric channel allocation algorithms in terms of throughput and packet drops.

• 한국지능시스템학회논문지
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• 제12권3호
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• pp.261-268
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• 2002

This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.