• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권3호
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• pp.735-748
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• 2014

The fault-tolerance routing problem is one of the most important issues in the application of the Internet of Things, and has been attracting growing research interests. In order to maintain the communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which alternate paths are created once the previous routing is broken. Then, we propose an improved efficient and intelligent fault-tolerance algorithm (IEIFTA) to provide the fast routing recovery and reconstruct the network topology for path failure in the Internet of Things. In the IEIFTA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by the immune mechanism, which can improve the ability of global search and improve the converging rate of the algorithm. The simulation results indicate that the IEIFTA-based fault-tolerance algorithm outperforms the EARQ algorithm and the SPSOA algorithm due to its ability of fast routing recovery mechanism and prolonging the lifetime of the Internet of Things.

• 대한전기학회논문지:전력기술부문A
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• 제55권6호
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• pp.249-254
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• 2006

A new algorithm for estimating power frequency is presented. Unlike conventional algorithms, the proposed algorithm is based on the fact that the magnitude gains of cosine and sine filters become different when the power frequency is deviated from the nominal value. This makes the algorithm capable of providing an accurate and fast estimate of the power frequency. To demonstrate the performance of the developed algorithm, various computer simulated data records are processed. The algorithm showed a high level of robustness as well as high measurement accuracy over a wide range of frequency changes. Moreover, the algorithm was highly immune to harmonics and noise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권4호
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• pp.1178-1191
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• 2014

The fault-tolerance routing problem is one of the most important issues in the application of the Internet of Things, and has been attracting growing research interests. In order to maintain the communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which alternate paths are created once the previous routing is broken. Then, we propose an improved efficient and intelligent fault-tolerance algorithm (IEIFTA) to provide the fast routing recovery and reconstruct the network topology for path failure in the Internet of Things. In the IEIFTA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by the immune mechanism, which can improve the ability of global search and improve the converging rate of the algorithm. The simulation results indicate that the IEIFTA-based fault-tolerance algorithm outperforms the EARQ algorithm and the SPSOA algorithm due to its ability of fast routing recovery mechanism and prolonging the lifetime of the Internet of Things.

• Journal of Construction Engineering and Project Management
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• 제7권2호
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• pp.1-10
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• 2017

The Clonal Selection Algorithm (CSA) is an algorithm inspired by the human immune system mechanism. In CSA, several parameters needs to be optimized by large amount of sensitivity analysis for the optimal results. They limit the accuracy of the results due to the uncertainty and subjectivity. Adaptive Clonal Selection (ACS), a modified version of CSA, is developed as an algorithm without controls by pre-defined parameters in terms of selection process and mutation strength. In this paper, we discuss the ACS in detail and present its implementation in construction site utilization planning (CSUP). When applied to a developed model published in research literature, it proves that the ACS are capable of searching the optimal layout of temporary facilities on construction site based on the result of objective function, especially when the parameterization process is considered. Although the ACS still needs some improvements, obtaining a promising result when working on a same case study computed by Genetic Algorithm and Electimze algorithm prove its potential in solving more complex construction optimization problems in the future.

• 경영과학
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• 제26권1호
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• pp.171-182
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• 2009

We consider the inventory replenishment problem and the vehicle routing problem simultaneously in the vending machine operation. This problem is known as the inventory routing problem. We design a memory cell in the clonal selection algorithm. The memory cell store the best solution of previous solved problem and use an initial solution for next problem. In general, the other clonal selection algorithm used memory cell for reserving the best solution in current problem. Experiments are performed for testing efficiency of the memory cell in demand uncertainty. Experiment result shows that the solution quality of our algorithm is similar to general clonal selection algorithm and the calculations time is reduced by 20% when the demand uncertainty is less than 30%.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.452-454
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• 2004

An accurate digital distance relaying algorithm which is immune to reactance effect of the fault resistance and the load current for phase-to-ground fault in 765kV untransposed transmission lines is proposed. The algorithm can estimate adaptively the impedance to a fault point independent of the fault resistance. To compensate the magnitude and phase of the apparent impedance, this algorithm uses the angle of an impedance deviation vector. The impedance correction algorithm for Phase-to-ground fault uses a voltage equation at fault point to compensate the fault current at fault point. A series of tests using EMTP output data in a 765kV untransposed transmission lines have proved the accuracy and effectiveness of the proposed algorithm.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.455-457
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• 2004

An accurate digital distance relaying algorithm which is immune to reactance effect of the fault resistance and the load current for phase-to-phase short fault in 765kV untransposed transmission lines is proposed. The algorithm can estimate adaptively the impedance to a fault point independent of the fault resistance. To compensate the magnitude and phase of the apparent impedance, this algorithm uses the angle of an impedance deviation vector. The impedance correction algorithm for phase-to-phase short fault uses a voltage equation at fault point to compensate the fault current at fault point. A series of tests using EMTP output data in a 765kv untransposed transmission lines have proved the accuracy and effectiveness of the proposed algorithm.

• Journal of Mechanical Science and Technology
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• 제17권12호
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• pp.1938-1948
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• 2003

This paper represents that an enhanced genetic algorithm (EGA) is applied to optimal design of a squeeze film damper (SFD) to minimize the maximum transmitted load between the bearing and foundation in the operational speed range. A general genetic algorithm (GA) is well known as a useful global optimization technique for complex and nonlinear optimization problems. The EGA consists of the GA to optimize multi-modal functions and the simplex method to search intensively the candidate solutions by the GA for optimal solutions. The performance of the EGA with a benchmark function is compared to them by the IGA (Immune-Genetic Algorithm) and SQP (Sequential Quadratic Programming). The radius, length and radial clearance of the SFD are defined as the design parameters. The objective function is the minimization of a maximum transmitted load of a flexible rotor system with the nonlinear SFDs in the operating speed range. The effectiveness of the EGA for the optimal design of the SFD is discussed from a numerical example.

• 전자공학회논문지CI
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• 제37권5호
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• pp.19-28
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• 2000

구륜 이동 로봇(Wheeled Mobile Robot)은 제어기 설계에 있어 Nonholonomic Constraints등에 의해 많은 어려움을 지닌다. 본 논문에서는 구륜 이동 로봇의 제어를 위해 PD와 퍼지 시스템이 결합된 제어기가 설계되며, 유전알고리즘에 기초되어 최적 퍼지시스템이 형성된다. 시스템의 최적화 과정은 독립적으로 수행되는 여러 단계들로 이루어지며, 각 단계마다 다른 형식의 알고리즘이 적용되며 효율적 탐색을 위해 Niche알고리즘 및 면역 알고리즘이 결합되어 적용된다. 각 출력용어집합은 최적의 원소들을 얻기 위해 수행되는 탐색에 의해 그 구성이 변화되며, 변화된 출력용어집합의 구성 원소와 관계된 규칙기반이 동시에 조절된다. 출력용어집합의 추가된 원소들 및 조절된 규칙에 대한 적합성이 평가되고 제어 성능의 향상에 기여하지 못한 부분들은 제거된다. 출력변수의 용어집합 및 규칙에 대한 반복적 조절 과정이 완료된 후, 입력 소속함수들에 대한 조정이 제약조건을 가지고 수행되며, 진화연산에 의한 출력소속함수들에 대한 조정이 수행된다.

• 한국지능시스템학회논문지
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• 제14권2호
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• pp.164-170
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• 2004

본 논문에서는 인공 면역 시스템과 분산 유전자 알고리즘에 기반하여 동작하는 자율분산로봇 시스템을 제안한다. 시스템에서 로봇들의 행동은 전역행동과 지역행동으로 분류된다. 전역행동은 환경에서 작업을 탐색하는데 이를 빠르게 수행하기 위하여 집합과 분산의 두 가지 행동으로 이루어져 있다. 이때 인공 면역 시스템은 로봇이 어떤 행동을 선택하여 행동할 것인가를 결정한다. 지역행동은 탐색된 작업을 수행하는 부분으로서 어떤 로봇들이 협조행동을 할지를 학습하고, 학습한 결과에 따라 작업을 수행하는 행동을 한다. 이를 위해 분산 유전자 알고리즘을 이용하여 각 로봇들은 주어진 작업에 대하여 학습을 한다. 제안된 시스템에서 학습 알고리즘은 주어지는 작업의 변화로봇들은 주어진 작업을 수행하기 위해 학습을 하고, 주어진 작업이 변할 경우 스스로 대처한다는 면에서 기존의 자율 분산 시스템보다 적응성에서 향상된 시스템이다.