• Journal of the Computational Structural Engineering Institute of Korea
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• v.23 no.5
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• pp.475-482
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• 2010

In this paper, an efficient two-level parallel domain decomposition algorithm is suggested to solve large-DOF structural problems. Each subdomain is composed of the coarse problem and local problem. In the coarse problem, displacements at coarse nodes are computed by the iterative method that does not need to assemble a stiffness matrix for the whole coarse problem. Then displacements at local nodes are computed by Multi-Frontal Sparse Solver. A parallel version of PCG(Preconditioned Conjugate Gradient Method) is developed to solve the coarse problem iteratively, which minimizes the data communication amount between processors to increase the possible problem DOF size while maintaining the computational efficiency. The test results show that the suggested algorithm provides scalability on computing performance and an efficient approach to solve large-DOF structural problems.

• Journal of Aerospace System Engineering
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• v.11 no.1
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• pp.41-46
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• 2017

A new test bench for small multi-rotor type drones has been developed. Six degrees of freedom (DOF) motion is possible due to a ball bushing, wheels, and rotating plates. An FPGA (field programmable gate array) based controller, that supports realtime parallel processing, is used to measure attitude with an accelerometer and a gyro to adjust motor speed. Several tests were performed to check the operational properties of the test bench and the controller. The results show that this test bench is proper for verifying controllers and the control methods of small multi-rotor drones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1100-1122
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• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

• ETRI Journal
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• v.32 no.1
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• pp.73-83
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• 2010

Combinatorial testing has been an active research area in recent years. One challenge in this area is dealing with the combinatorial explosion problem, which typically requires a very expensive computational process to find a good test set that covers all the combinations for a given interaction strength (t). Parallelization can be an effective approach to manage this computational cost, that is, by taking advantage of the recent advancement of multicore architectures. In line with such alluring prospects, this paper presents a new deterministic strategy, called multicore modified input parameter order (MC-MIPOG) based on an earlier strategy, input parameter order generalized (IPOG). Unlike its predecessor strategy, MC-MIPOG adopts a novel approach by removing control and data dependency to permit the harnessing of multicore systems. Experiments are undertaken to demonstrate speedup gain and to compare the proposed strategy with other strategies, including IPOG. The overall results demonstrate that MC-MIPOG outperforms most existing strategies (IPOG, IPOF, IPOF2, IPOG-D, ITCH, TConfig, Jenny, and TVG) in terms of test size within acceptable execution time. Unlike most strategies, MC-MIPOG is also capable of supporting high interaction strengths of t > 6.

• Journal of Korean Institute of Industrial Engineers
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• v.35 no.4
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• pp.257-265
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• 2009

This paper considers the hybrid flow shop scheduling problem for the objective of minimizing the number of tardy jobs. In hybrid flow shops, each job is processed through multiple production stages in series, each of which has multiple identical parallel machines. The problem is to determine the allocation of jobs to the parallel machines at each stage as well as the sequence of the jobs assigned to each machine. Due to the complexity of the problem, we suggest search heuristics, tabu search and simulated annealing algorithms with a new method to generate neighborhood solutions. In particular, to evaluate and select neighborhood solutions, three surrogate objectives are additionally suggested because not much difference in the number of tardy jobs can be found among the neighborhoods. To test the performances of the surrogate objective based search heuristics, computational experiments were performed on a number of test instances and the results show that the surrogate objective based search heuristics were better than the original ones. Also, they gave the optimal solutions for most small-size test instances.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.9
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• pp.823-829
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• 2012

An excitation table is commonly used for vibration and ride tests for parts or assemblies of automobiles, aircrafts, or other heavy systems. The authors have analyzed several kinematic properties of an excitation table that is under development for heavy transport vehicles. It consists of one table and 7 linear hydraulic actuators. The authors have performed mobility analysis, inverse kinematics, forward kinematics, and singularity analysis. Especially, we have proposed a fast forward kinematic solution considering the limited motion of the excitation table. On the assumption that the motion variables such as rotation angles and displacements are small, the forward kinematic problem is converted to the observer problem of a linear system. This provides a fast solution. Also we have verified that there are no singularity points in the working range by numerical analysis.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2630-2637
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• 2020

The structure design of divertor Multi-Functional Maintenance Platform (MFMP) actuated by hydraulic system for China Fusion Engineering Test Reactor (CFETR) was introduced in this paper. The model of MFMP was established according to maintenance requirements. In this paper, Newton-Euler method and the improved virtual work principle were used, the equivalent driving force of each actuator was obtained through the equivalent Jacobian inverse matrix derived from velocity relationship among the components. The accuracy of the model was verified by ADAMS simulation. The stability control of the heavy-duty components driven by hydraulic cylinders based on Newton-Euler method and improved virtual work principle was established.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.3
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• pp.444-450
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• 1996

A digital hardware architecture for artificial neural network with learning capability is described in this paper. It is a modified hardware architecture known as HANNIBAL(Hardware Architecture for Neural Networks Implementing Back propagation Algorithm Learning). For implementing an efficient neural network hardware, we analyzed various type of multiplier which is major function block of neuro-processor cell. With this result, we design a efficient digital neural network hardware using serial/parallel multiplier, and test the operation. We also analyze the hardware efficiency with logic level simulation. (author). refs., figs., tabs.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.170-175
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• 2001

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative subcycles. In analyzing or optimizing such a coupled system, it is essential to determine the best order of the processes within these subcycles to reduce design cycle time and cost. This is accomplished by decomposing large multidisciplinary problems into several multidisciplinary analysis subsystems (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problems to improve design efficiency by using the multiple objective genetic algorithm (MOGA), and a sample test case is presented to show the effects of optimizing the sequence with MOGA.

• Korean Management Science Review
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• v.14 no.2
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• pp.47-61
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• 1997

This paper considers the parallel machines scheduling problem characterized as a multi-objective combinatorial problem. As this problem belongs to the NP-complete problem, genetic algorithms are applied instead of the traditional analytical approach. The purpose of this study is to show how the problem can be effectively solved by using genetic algorithms with a permutation approach. First, a permutation representation which can effectively represent the chromosome is introduced for this problem . Next, a schedule builder which employs the combination of scheduling theories and a simple heuristic approach is suggested. Finally, through the computer experiments of genetic algorithm to test problems, we show that the niche formation method does not contribute to getting better solutions and that the PMX crossover operator is the best among the selected four recombination operators at least for our problem in terms of both the performance of the solution and the operational convenience.