• Journal of Computational Design and Engineering
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• v.3 no.2
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• pp.132-139
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• 2016

In this paper, a minimum time path planning strategy is proposed for multi points manufacturing problems in drilling/spot welding tasks. By optimizing the travelling schedule of the set points and the detailed transfer path between points, the minimum time manufacturing task is realized under fully utilizing the dynamic performance of robotic manipulator. According to the start-stop movement in drilling/spot welding task, the path planning problem can be converted into a traveling salesman problem (TSP) and a series of point to point minimum time transfer path planning problems. Cubic Hermite interpolation polynomial is used to parameterize the transfer path and then the path parameters are optimized to obtain minimum point to point transfer time. A new TSP with minimum time index is constructed by using point-point transfer time as the TSP parameter. The classical genetic algorithm (GA) is applied to obtain the optimal travelling schedule. Several minimum time drilling tasks of a 3-DOF robotic manipulator are used as examples to demonstrate the effectiveness of the proposed approach.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.314-317
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• 2002

This paper present a methods for performance evaluation of digital governor and we treat the way to reduce the trial and error when the developed digital governing system is applied to station. The parameters of the model are estimated using input-output data, the model adjustment technique and a genetic algorithm(GA). To verify validity of the completed model, the comparision model and real output signals have been carried out. The developed digital governing system is applied to Sumjingang hydro-power plant, Korea Hydro Nuclear Power Corporation, it has replaced mechanical governing system. Test was performed in order to demonstrate the performance evaluation of the digital governor parameters.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.1
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• pp.32-39
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• 2012

With the aid of genetic algorithm (GA), global optimization with multiple geometry parameters is feasible in the design of switched flux permanent magnet (SFPM) machines. To investigate the advantages of global optimization over individual optimization, which has been used extensively for the design of SFPM machines, a comparison between the two approaches is carried out for the case of fixed copper loss and volume. In the case of individual parameter optimization, the sequence in which the individual parameters are optimized is very important. In the global optimization a better design can always be achieved although the corresponding torque density is found to be only slightly better than that of individually optimized with correct design sequence. By using the obtained global optimization results, the performance in machines having two types of stator and rotor pole combinations, i.e. 12/10 and 12/14, are compared, and it is shown that higher torque is exhibited in the 12/14 SFPM machine. Finally, this paper also demonstrates that global optimization, with the restriction of equal pole width, magnet thickness and slot opening, can maximize the torque density without significantly sacrificing other performance, such as cogging torque and overload capability.

• Nuclear Engineering and Technology
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• v.52 no.10
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• pp.2387-2393
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• 2020

In this study, a gradient material for shielding neutrons and gamma rays was developed, which consists of epoxy resin, boron carbide (B₄C), lead (Pb) and a little graphene oxide. It aims light weight and compact, which will be applied on the transportable nuclear reactor. The material is made up of sixteen layers, and the thickness and components of each layer were designed by genetic algorithm (GA) combined with Monte Carlo N Particle Transport (MCNP). In the experiment, the viscosities of the epoxy at different temperatures were tested, and the settlement regularity of Pb particles and B₄C particles in the epoxy was simulated by matlab software. The material was manufactured at 25 ℃, the Pb C and O elements of which were also tested, and the result was compared with the outcome of the simulation. Finally, the material's shielding performance was simulated by MCNP and compared with the uniformity material's. The result shows that the shielding performance of gradient material is more effective than that of the uniformity material, and the difference is most noticeable when the materials are 30 cm thick.

• Earthquakes and Structures
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• v.13 no.1
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• pp.51-58
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• 2017

Passive dynamic vibration absorbers (DVAs) are often used to suppress the excessive vibration of a large structure due to their simple construction and low maintenance cost compared to other vibration control techniques. A new type of passive DVA consists of two pendulums connected with spring and dashpot element is investigated. This research evaluated the performance of the DVA in reducing the vibration response of a two degree of freedom shear structure. A model for the two DOF vibration system with the absorber is developed. The nominal absorber parameters are calculated using a Genetic Algorithm(GA) procedure. A parametric study is performed to evaluate the effect of each absorber parameter on performance. The simulation results show that the optimum condition for the absorber frequencies and damping ratios is mainly affected by pendulum length, mass, and the damping coefficient of the pendulum's hinge joint. An experimental model validates the theoretical results. The simulation and experimental results show that the proposed technique is able be used as an effective alternative solution for reducing the vibration response of a multi degree of freedom vibration system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.4
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• pp.102-108
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• 1999

The sliding rmde controller(SMC) is known as having the robust variable structures for the nonlinear control systems such as coupled tank system with the pararretric perturbations and with the rapid disturbances. But the adaptive tuning algorit1uns for their pararreters are not satisfactory. Therefore, in this paper, a Real Variable Elitist Genetic Algorithm based Sliding Mode Controller (RVEGA SMC) for the precise control of the coupled tank level was tried. The SMC's switching pararreters were optimized easily and rapidly by RVEGA The simulation results showed that the tank level could be satisfactorily controlled without any overshoot and any steady-state error by the proposed RVEGA SMC.GA SMC.

• Journal of Power Electronics
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• v.15 no.2
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• pp.469-478
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• 2015

In order to promote the application of switched reluctance machines (SRM) in electric vehicles (EVs), the braking torque closed-loop control of a SRM is proposed. A hysteresis current regulator with the soft chopping mode is employed to reduce the switching frequency and switching loss. A torque estimator is designed to estimate the braking torque online and to achieve braking torque feedback. A feed-forward plus saturation compensation torque regulator is designed to decrease the dynamic response time and to improve the steady-state accuracy of the braking torque. The turn-on and turn-off angles are optimized by a genetic algorithm (GA) to reduce the braking torque ripple and to improve the braking energy feedback efficiency. Finally, a simulation model and an experimental platform are built. The simulation and experimental results demonstrate the correctness of the proposed control strategy.

• Nuclear Engineering and Technology
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• v.52 no.1
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• pp.178-184
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• 2020

The traditional methods for radiation shield design always only focus on either the structure or the components of the shields rather than both of them at the same time, which largely affects the shielding performance of the facilities, so in this paper, a novel method for designing the structure and components of shields simultaneously is put forward to enhance the shielding ability. The method is developed by using the genetic algorithm (GA) and the MCNP software. In the research, six types of shielding materials with different combinations of elements such as polyethylene (PE), lead (Pb) and Boron compounds are applied to the radiation shield design, and the performance of each material is analyzed and compared. Then two typical materials are selected based on the experiment result of the six samples, which are later verified by the Compact Accelerator Neutron Source (CANS) facility. By using this method, the optimal result can be reached rapidly, and since the design progress is semi-automatic for most procedures are completed by computer, the method saves time and improves accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.24-24
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• 2015

최근 원격탐사기법을 이용한 많은 가뭄평가기법들이 개발되었으나 산림과 함께 산악지형이 우세한 우리나라의 경우 지형특성으로 인하여 가뭄평가시 불확실성이 증가하게 된다. 특히, 농업가뭄은 기후와 지표특성에 큰 영향을 받기 때문에 기후특성만을 고려한 가뭄지수는 실제 필요한 농업가뭄의 특성을 반영하는데 있어서 한계가 있다. 따라서 본 연구에서는 기후와 지표특성을 함께 고려할 수 있는 토양수분을 이용한 가뭄평가기법(Drought Assessment Scheme)을 개발하였다. 가뭄평가기법을 위하여 역추적기법(Inverse Modeling-IM) 기반의 자료동화기법(Data Assimilation, DA)을 이용하였다. 자료동화기법은 1-Dimensional (1-D) 기반의 토양의 물리적 특성을 고려하는 SWMI_ST 모형과 최적화 알고리즘(유전자 알고리즘, Genetic Algorithm-GA)을 연계하여 실측 및 위성기반의 토양수분자료로부터 토양의 수리학적 매개변수(${\alpha}$, n, ${\Theta}_{res}$, ${\Theta}_{sat}$, $K_{sat}$)를 추출한다. 본 연구에서는 LANDSAT(30 m X 30 m) 및 MODerate Resolution Imaging Spectroradiometer(MODIS, 500 m X 500 m) 이미지자료를 이용하여 시 공간적으로 분포되어 있는 토양수분을 산정하였으며, 이후 자료동화기법을 이용하여 LANDSAT/MODIS 토양수분자료로 부터 공간적으로 분포되어 있는 토양의 매개변수를 추출하였다. 추출된 매개변수, GIS 기반의 지표피복 및 기상자료를 이용하여 장기간의 토양수분을 산정 및 예측 할 수 있다. 고해상도의 이미지 자료를 사용하는 가뭄평가기법은 필지~시 군 단위까지 실제 우리나라 지형특성을 고려하여 효율적으로 가뭄을 모니터링 및 예측 할 수 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.392-395
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• 2017

본 연구에서는 일 증발접시 증발량 산정을 위한 딥러닝 (deep learning) 모형의 적용성을 평가하였다. 본 연구에서 적용된 딥러닝 모형은 deep belief network (DBN) 기반 deep neural network (DNN) (DBN-DNN) 모형이다. 모형 적용성 평가를 위하여 부산 관측소에서 측정된 기상자료를 활용하였으며, 증발량과의 상관성이 높은 기상변수들 (일사량, 일조시간, 평균지상온도, 최대기온)의 조합을 고려하여 입력변수집합 (Set 1, Set 2, Set 3)별 모형을 구축하였다. DBN-DNN 모형의 성능은 통계학적 모형성능 평가지표 (coefficient of efficiency, CE; coefficient of determination, $r^2$; root mean square error, RMSE; mean absolute error, MAE)를 이용하여 평가되었으며, 기존의 두가지 형태의 ANN (artificial neural network), 즉 모형학습 시 SGD (stochastic gradient descent) 및 GD (gradient descent)를 각각 적용한 ANN-SGD 및 ANN-GD 모형과 비교하였다. 효과적인 모형학습을 위하여 각 모형의 초매개변수들은 GA (genetic algorithm)를 이용하여 최적화하였다. 그 결과, Set 1에 대하여 ANN-GD1 모형, Set 2에 대하여 DBN-DNN2 모형, Set 3에 대하여 DBN-DNN3 모형이 가장 우수한 모형 성능을 나타내는 것으로 분석되었다. 비록 비교 모형들 사이의 모형성능이 큰 차이를 보이지는 않았으나, 모든 입력집합에 대하여 DBN-DNN3, DBN-DNN2, ANN-SGD3 순으로 모형 효율성이 우수한 것으로 나타났다.