• 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.

• Journal of the Korean Society of Propulsion Engineers
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• v.10 no.4
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• pp.54-60
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• 2006

A method for generating the compressor map from some performance measuring data using the hybrid intelligent technique was newly proposed. In order to improve accuracy of the traditional scaling method, a method to generate the compressor map using the GAs(Genetic Algorithms) was previously proposed, but the method has a drawback that it can not find correctly surge and choke points of the compressor map. However, the proposed hybrid intelligent method can determine obviously those points as well as improve the accuracy of the compressor map through complementarily using the GAs and the scaling method.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.57-61
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• 2013

A hybrid genetic algorithm based learning method for the morphological neural networks (MNN) is proposed. The morphological neural networks are based on max-plus algebra, therefore, it is difficult to optimize the coefficients of MNN by the learning method with derivative operations. In order to solve the difficulty, a hybrid genetic algorithm based learning method to optimize the coefficients of MNN is used. Through the image compression/reconstruction experiment using test images extracted from standard image database(SIDBA), it is confirmed that the quality of the reconstructed images obtained by the proposed method is better than that obtained by the conventional neural networks.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.5
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• pp.53-64
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• 2019

The term 're-use' here signifies the re-use of end-of-life products without changing their form after they have been thoroughly inspected and cleaned. In the re-use network model, the distributor determines the product order quantity on the network through which new products are received from the suppliers or products are supplied to the customers through re-use of the recovered products. In this paper, we propose a reusable network model for reusable products that considers the total logistics cost from the forward logistics to the reverse logistics. We also propose a reusable network model that considers the processing and disposal costs for reuse in an optimal inventory management environment. The authors employe Genetic Algorithm (GA), which is one of the optimization techniques, to verify the validity of the proposed model. And in order to investigate the effect of the parameters on the solution, the priority-based GA (priGA) under three different parameters and the modified Hybrid GA (mhGA), in which parameters are adjusted for each generation, were applied to four examples with varying sizes in the simulation.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.693-703
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• 2014

When setting up an experiment for extrapolation at multiple points outside the design space, we often face a difficulty in which point we should emphasize even if the polynomial model under consideration is given. In this paper we propose various methods under two possible scenarios that deal with extrapolations. One considered in this paper is the situation when the model assumed can be extended beyond the design space. In this setting, the many classical methods(including various approaches the authors proposed before) were revisited in the context of extrapolation. But the real problem arises when there is an uncertainty concerning the validity of the assumed model. Therefore, the second scenario is to develop an appropriate procedure when we have limited information about model. Consequently, a hybrid approach is suggested to deal with this issue of how to handle the multiple extrapolating under model uncertainty. A search algorithm was implemented because the classical exchange algorithm was found difficult to handle the complexity of the problem.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.487-493
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• 2013

As the spread of smart devices that service variety of content, limited mobile terminal channel assignment problem has intensified. In the channel assignment in mobile networks mobile switching center at the request belongs to each base station allocates the channel to the mobile station. This effectively allocate the limited channels of various methods have been proposed for, in this case a hybrid channel allocation using genetic algorithms UHGA (Universal Hybrid Channel Assignment using Genetic Algorithm) in rural areas or urban areas, such as universal network applied to a variety of environments that the efficiency is verified through simulation.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.4
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• pp.425-433
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• 2022

This paper studies the method of delivering munitions using both drones and UGVs that are developing along with the 4th Industrial Revolution. While drones are more mobile than UGVs, their loading capacity is small, and UGVs have relatively less mobility than drones, but their loading capacity is better. Therefore, by simultaneously operating these two delivery means, each other's shortcomings may be compensated. In addition, on actual battlefields, time constraints are an important factor in delivering munitions. Therefore, assuming an actual battlefield environment with a time limit, we establish delivery routes that minimize delivery time by operating both drones and UGVs with different capacities and speeds. If the delivery is not completed within the time limit, penalties are imposed. We devised the hybrid genetic algorithm to find solutions to the proposed model, and as results of the experiment, we showed the algorithm we presented solved the actual size problems in a short time.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.292-293
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• 2008

많은 분야에서 최적의 기준을 바탕으로 특징들의 부분집합을 선택하는 문제들이 핵심 요소로 작용하고 있다. 다양한 특징들의 부분집합 중에서 가능한 한 가장 성능이 우수한 특징들의 부분집합을 선택하기 위해서는 특징선택 방법이 알고리즘과 적용분야들을 고려해야한다. 이 논문에서는 특징선택을 위해서 서로 다른 두 종류의 최적화 문제를 탐색하는 방법을 제안하고, 그 결과를 실험으로 보여준다.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.565-579
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• 2016

In this paper a novel cancer subtype's classifier based on Hybrid Samples Balanced Genetic Algorithm with Extreme Learning Machine (hSBGA-ELM) is presented. Proposed cancer subtype's classifier uses genes' expression data of 16063 genes from open Global Cancer Map (GCM) data base for accurate cancer subtype's classification. Proposed method efficiently classifies 14 subtypes of cancer (breast, prostate, lung, colorectal, lymphoma, bladder, melanoma, uterus, leukemia, renal, pancreas, ovary, mesothelioma and CNS). Proposed hSBGA-ELM unifies genes' selection procedure and cancer subtype's classification into one framework. Proposed Hybrid Samples Balanced Genetic Algorithm searches a reduced robust set of genes responsible for cancer subtype's classification from 16063 genes available in GCM data base. Selected reduced set of genes is used to build cancer subtype's classifier using Extreme Learning Machine (ELM). As a result, reduced set of robust genes guarantees stable generalization performance of the proposed cancer subtype's classifier. Proposed hSBGA-ELM discovers 95 genes probably responsible for cancer. Comparison with existing cancer subtype's classifiers clear indicates efficiency of the proposed method.

• Journal of Navigation and Port Research
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• v.35 no.1
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• pp.83-91
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• 2011

The accuracy of forecasting is remarkably important to reduce total cost or to increase customer services, so it has been studied by many researchers. In this paper, the artificial neural network (ANN), one of the most popular nonlinear forecasting methods, is compared with autoregressive integrated moving average(ARIMA) model through performing a prediction of container traffic. It uses a hybrid methodology that combines both the linear ARIAM and the nonlinear ANN model to improve forecasting performance. Also, it compares the methodology with other models in performance for prediction. In designing network structure, this work specially applies the genetic algorithm which is known as the effectively optimal algorithm in the huge and complex sample space. It includes the time delayed neural network (TDNN) as well as multi-layer perceptron (MLP) which is the most popular neural network model. Experimental results indicate that both ANN and Hybrid models outperform ARIMA model.