• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.6
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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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• v.8 no.4
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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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• v.7 no.2
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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.

• Korean Management Science Review
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• v.26 no.1
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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%.

• Proceedings of the KIEE Conference
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• summer
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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.

• Proceedings of the KIEE Conference
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• summer
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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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• v.17 no.12
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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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.5
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• pp.19-28
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• 2000

It is not so easy to control the wheeled mobile robot because of some causes like non-holonomic constraints. To overcome these problems, a controller that PD system is combined with fuzzy process is composed of several steps that have each separate algorithm and niche search algorithm and immune algorithm is applied partly. Output term set is changed by search that is performed to get optimal elements and then the rule base is also reformed. The fitness for the altered system is estimated and the surplus elements are removed. After the adjustment of output term set and rule base is finished, input and output membership functions is tuned.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.164-170
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• 2004

This paper proposes a Distributed Autonomous Robotic System(AIS) based on Artificial Immune System(AIS) and Distributed Genetic Algorithm(DGA). The behaviors of robots in the system are divided into global behaviors and local behaviors. The global behaviors are actions to search tasks in environment. These actions are composed of two types: dispersion and aggregation. AIS decides one among above two actions, which robot should select and act on in the global. The local behaviors are actions to execute searched tasks. The robots learn the cooperative actions in these behaviors by the DGA in the local. The proposed system is more adaptive than the existing system at the viewpoint that the robots learn and adapt the changing of tasks.