• Journal of Advanced Marine Engineering and Technology
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• v.22 no.4
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• pp.498-504
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• 1998

This paper presents an optimal control algorithm for the overhead crane. To control the swing motion and the position tracking of the payload of the overhead crane a state feedback control algorithm is applied. by using a hybrid genetic algorithm the feedback gains of the state feedback is optimized to minimize the cost function composed of position errors and payload swing angle under unknown constant disturbances. Computer simulation is performed to demonstrate the effectiveness of the proposed control algorithm.

• International Journal of Ocean System Engineering
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• v.2 no.4
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• pp.233-238
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• 2012

In this study, we carried out a precedent investigation for an ultra-large container ship, which is expected to be a higher value-added vessel. We studied a preliminary optimized design technique for estimating the principal dimensions of an ultra-large container ship. Above all, we have developed optimized dimension estimation models to reduce the building costs and weight, using previous container ships in shipbuilding yards. We also applied a generalized estimation model to estimate the shipping service costs. A Genetic Algorithm, which utilized the RFR (required freight rate) of a container ship as a fitness value, was used in the optimization technique. We could handle uncertainties in the shipping service environment using a Monte-Carlo simulation. We used several processes to verify the estimated dimensions of an ultra-large container ship. We roughly determined the general arrangement of an ultra-large container ship up to 1500 TEU, the capacity check of loading containers, the weight estimation, and so on. Through these processes, we evaluated the possibility for the practical application of the preliminary design model.

• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.3
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• pp.3-13
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• 2005

The conceptual rainfall-runoff models are used to predict complex hydrological effects of a basin. However, to obtain reliable results, there are some difficulties and problems in choosing optimum model, calibrating, and verifying the chosen model suitable for hydrological characteristics of the basin. In this study, Genetic Algorithm and SCE-UA method as global optimization methods were applied to compare the each optimization technique and to analyze the application for the rainfall-runoff models. Modified TANK model that is used to calculate outflow for watershed management and reservoir operation etc. was optimized as a long term rainfall-runoff model. And storage-function model that is used to predict real-time flood using historical data was optimized as a short term rainfall-runoff model. The optimized models were applied to simulate runoff on Pyeongchang-river watershed and Bocheong-stream watershed in 2001 and 2002. In the historical data study, the Genetic Algorithm and the SCE-UA method showed consistently good results considering statistical values compared with observed data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.9
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• pp.761-769
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• 2008

The majority of mechanical parts are manufactured by milling machines. Hence, geometrically efficient algorithms for tool path generation and physical considerations for better machining productivity with guarantee of machining safety are the most important issues in milling tasks. In this paper, an optimized path generation algorithm for direction parallel milling which is commonly used in the roughing stage is presented. First of all, a geometrically efficient tool path generation algorithm using an intersection points-graph is introduced. Although the direction parallel tool path obtained from geometric information have been successful to make desirable shape, it seldom consider physical process concerns like cutting forces and chatters. In order to cope with these problems, an optimized tool path, which maintains constant MRR in order to achieve constant cutting forces and to avoid chatter vibrations at all time, is introduced and the result is verified. Additional tool path segments are appended to the basic tool path by using a pixel based simulation technique. The algorithm has been implemented for two dimensional contiguous end milling operations, and cutting tests are conducted by measuring spindle current, which reflects machining situations, to verify the significance of the proposed method.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.740-743
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• 1998

This paper presents an optimization algorithm for a stable Dynamic Neural Network (DNN) using genetic algorithm. Optimized DNN is applied to a problem of controlling nonlinear dynamical systems. DNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDNN has considerably fewer weights than DNN. The object of proposed algorithm is to the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling, a nonlinear dynamic system using the proposed optimized SDNN considering stability' is demonstrated by case studies.

• Transactions of Materials Processing
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• v.28 no.4
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• pp.203-211
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• 2019

In this study, the weight of type IV pressure vessel is optimized through the burst pressure condition using the finite element analysis (FEA) based on the genetic algorithm (GA). The optimization design variables include the thickness of composite layers and the winding angles. The optimized design variables are validated using the numerical simulations for the pressure vessel. Consequently, the weight is decreased by about 6.5% as compared to the previously reported results for Type III pressure vessel. Additionally, a method which reduces the entire optimization time is proposed. In the original method, the population size is constant across all generations. However, the proposed method could reduce the workload through the reduction of the population size by half for every 25 generations. Thus, the proposed method is observed to increase the weight by about 0.1%, however, the working time for the optimization could be decreased by about 46.5%.

• Journal of Korea Multimedia Society
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• v.10 no.1
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• pp.83-93
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• 2007

WFQ (Weighted Fair Queuing) is the most popular fair queuing algorithm, but it had the inherent drawback of a poet bandwidth utilization, particularly under the traffic requiring a low rate but tight delay bound such as internet phone. It was recently identified that the poor utilization is mainly due to the non-optimized latency of a flow and then LOFQ(Latency-Optimized Fair Queuing) to overcome the drawback was introduced. In this paper, we improve the performance of LOFQ by introducing an occupied resource optimization function and reduce the implementation complexity of recursive resource transformation by revising the transformation scheme. We also prove the superiority of LOFQ over WFQ in terms of utilization. The simulation result shows that the improved LOFQ provides $20{\sim}30%$ higher utilization than that in the legacy LOFQ.

• The KIPS Transactions:PartC
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• v.14C no.2
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• pp.155-162
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• 2007

WFQ (Weighted Fair Queuing) is the most popular fair queuing algorithm, but it had the inherent drawback of a poor bandwidth utilization, particularly under the traffic requiring a low rate but tight delay bound such as internet phone, It was recently identified that the poor utilization is mainly due to non optimized latency of a flow and then LOFQ(Latency-Optimized Fair Queuing) to overcome the drawback was introduced, LOFQ was also improved through introducing an occupied resource optimization function and the implementation complexity of recursive resource transformation was reduced with revising the transformation scheme. However, the performance of LOFQ has been evaluated by means of simulation, so that there are some difficulties in evaluating the performance in the terms of the accuracy and evaluation time, In this paper, we develop how to analytically compute the bandwidth utilization in LOFQ.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2891-2893
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• 2005

In this paper, we introduce a new category of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multi-layer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. The conventional SOFPNN algorithm leads to a tendency to produce overly complex networks as well as a repetitive computation load by the trial and error method and/or the a repetitive parameter adjustment by designer. In order to generate a structurally and parametrically optimized network, such parameters need to be optimal. In this study, in solving the problems with the conventional SOFPNN, we introduce a new design approach of evolutionary optimized SOFPNN. Optimal parameters design available within FPN (viz. the no. of input variables, the order of the polynomial, input variables, and the no. of membership function) lead to structurally and parametrically optimized network which is more flexible as well as simpler architecture than the conventional SOFPNN. In addition, we determine the initial apexes of membership functions by genetic algorithm.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.10a
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• pp.199-204
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• 2000

In this study, an optimum design algorithm using efficient reanalysis is proposed for seismic design of RC Piers. The proposed algorithm for optimization of RC Piers is based on efficient reanalysis technique. Considering structural behavior of RC Piers, several other approximation techniques, such as artificial constraint deletion is introduced to increase the efficiency of optimization. The efficiency and robustness of the proposed algorithm increase the proposed reanalysis technique is demonstrated by comparing it with a conventional optimization algorithm. A few of design examples are optimized to show the applicability of the proposed algorithm.