• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1871-1875
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• 1997

Crane operation is manually accomplished by skilled operators. Recently, the concept of automation is widely introduced in shipping and unloading operation using the overhead crane for the enhanced productivity. In this regards, we designed an angle detector and 3D position detectro which are key evices for this operation. As well as an intellignet control algorithm is developed for the implementation of swing free crane. The performance of the presented algorithm is tested for the swing angle and the position of the overheas crand. The control scheme adopts a feedback control of an angular velocity of swing in initial phase and then the fuzzy controller whose rule base is optimized by a genetic algorithm.

• Journal of Korean Association for Spatial Structures
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• v.16 no.3
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• pp.79-88
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• 2016

An outrigger damper system has been proposed to reduce dynamic responses of tall buildings. In previous studies, an outrigger damper system was optimally designed to decrease a wind-induced or earthquake-induced dynamic response. When an outrigger damper system is optimally designed for wind excitation, its control performance for seismic excitation deteriorates. Therefore, a smart outrigger damper system is proposed in this study to make a control system that can simultaneously reduce both wind and seismic responses. A smart outrigger system is made up of MR (Magnetorheological) dampers. A fuzzy logic control algorithm (FLC) was used to generate command voltages sent for smart outrigger damper system and the FLC was optimized by genetic algorithm. This study shows that the smart outrigger system can provide good control performance for reduction of both wind and earthquake responses compared to the general outrigger system.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1544-1550
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• 2014

In this paper, an adaptive optimization strategy utilizing Kriging model and genetic algorithm is proposed for the optimal design of electromagnetic devices. The ordinary Kriging assisted by the spherical covariance model is used to construct surrogate models. In order to improve the computational efficiency, the adaptive uniform sampling strategy is applied to generate sampling points in design space. Through several iterations and gradual refinement process, the global optimal point can be found by genetic algorithm. The proposed algorithm is validated by application to the optimal design of a switched reluctance motor, where the stator pole face and shape of pole shoe attached to the lateral face of the rotor pole are optimized to reduce the torque ripple.

• The KIPS Transactions:PartD
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• v.16D no.5
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• pp.729-736
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• 2009

The accurate estimation of software development cost is important to a successful development in software engineering. Until recent days, the model using regression analysis based on statistical algorithm and machine learning method have been used. However, this paper estimates the software cost using support vector regression, a sort of machine learning technique. Also, it finds the best set of optimized parameters applying genetic algorithm. The proposed GA-SVR model outperform some recent results reported in the literature.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3474-3497
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• 2016

Network Function Virtualization (NFV) and Software Defined Networking (SDN) are recently considered as very promising drivers of the evolution of existing middlebox services, which play intrinsic and fundamental roles in today's networks. To address the virtual service deployment issues that caused by introducing NFV or SDN to networks, this paper proposes an optimal solution by combining quantum genetic algorithm with cooperative game theory. Specifically, we first state the concrete content of the service deployment problem and describe the system framework based on the architecture of SDN. Second, for the service location placement sub-problem, an integer linear programming model is built, which aims at minimizing the network transport delay by selecting suitable service locations, and then a heuristic solution is designed based on the improved quantum genetic algorithm. Third, for the service amount placement sub-problem, we apply the rigorous cooperative game-theoretic approach to build the mathematical model, and implement a distributed algorithm corresponding to Nash bargaining solution. Finally, experimental results show that our proposed method can calculate automatically the optimized placement locations, which reduces 30% of the average traffic delay compared to that of the random placement scheme. Meanwhile, the service amount placement approach can achieve the performance that the average metric values of satisfaction degree and fairness index reach above 90%. And evaluation results demonstrate that our proposed mechanism has a comprehensive advantage for network application.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1327-1336
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• 2017

In this study, the problems in the short term stock market forecasting are analyzed and the feasibility of the ARIMA method and the backpropagation neural network is discussed. Neural network and genetic algorithm in short term stock forecasting is also examined. Since the backpropagation algorithm often falls into the local minima trap, we optimized the backpropagation neural network and established a genetic algorithm based on backpropagation neural network for forecasting model in order to achieve high forecasting accuracy. The experiments adopted the korea composite stock price index series to make prediction and provided corresponding error analysis. The results show that the genetic algorithm based on backpropagation neural network model proposed in this study has a significant improvement in stock price index series forecasting accuracy.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.501-504
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• 1995

A Simultaneous optimal design of structural and control system of a semi-active suspension is applied on a helf-car model in this paper. Suspension stiffnesses and dampings are selected as structural design parameters and damping forces of variable dampers as controller parameters. Sence this optimization problem is of large discontinuous space, conventional exhaustive methods are not enough. So we here try out an approach using Genetic Algorithm for our problem. Through numerical simulation work, the performance of the simultaneously optimized system was tested and showed meaningful improvement over the partially optimized ones.

• Proceedings of the Optical Society of Korea Conference
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• 2007.02a
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• pp.167-168
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• 2007

A novel holographic demultiplexer with multi-group has been firstly proposed and experimentally demonstrated using optimized CGH by genetic algorithm. For experimental demonstrations, a LCOS with 8.1um pixel size and spatial resolution of 1920X1200 is used.

• Structural Engineering and Mechanics
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• v.47 no.5
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• pp.729-740
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• 2013

This paper addresses the structural design of the front end of Siemens ER24 locomotive body. The steel structure of the frontend is replaced with composite. Optimization of the composite lay-up is performed using Genetic Algorithms. Initially an optimized single design for the entire structure is presented. Then a more refined optimum is developed by considering the separate optimization of 7 separate regions of the structure. Significant savings in the weight of the structure are achieved.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.243-246
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• 1996

Modular robot manipulator is a robotic system assembled from discrete joints and links into one of many possible manipulator configurations. This paper describes the design method of newly developed modular robot manipulator and the methodology of a task based reconfiguration of it. New locking mechanism is proposed and it provides quick coupling and decoupling. A parallel connection method is devised and it makes modular robot manipulator working well and the number of components on each module reduced. To automatically determine a sufficient or optimal arrangement of the modules for a given task, we also devise an algorithm that automatically generates forward and inverse manipulator kinematics, and we propose an algorithm which maps task specifications to the optimized manipulator configurations. Efficient genetic algorithms are generated and used to search for a optimal manipulator from task specifications. A few of design examples are shown.