• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.116-116
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• 2017

지하수 인공함양은 수자원확보 및 비상시 용수를 목적으로 세계적으로 연구 및 프로젝트가 진행되고 있다. 인공함양의 방법에는 여러 가지의 방식이 있지만 본 연구는 관정을 통해 주입하는 ASTR(Aquifer Storage Transfer and Recovery)방법을 이용하여 연구하였다. ASTR이란 지표수를 인공적인 방법으로 대수층에 주입시키고, 일정기간 저장시키거나 유하시킨 후 양수하는 방법이다. 염수로 포화된 피압대수층에 담수를 주입하여 염수를 치환할 수 있는 주입-양수 시스템을 연구하였다. 염수로 포화된 대수층에서 인공함양기술을 성공시키기 위해서는 양수정으로 유입되는 염수비율이 0%을 만족하며, 주입으로 인한 수위상승량은 지반변형을 일으키지 않는 최소한의 값을 가지는 것이다. 본 연구는 앞서 언급한 인공함양기술을 성공시키기 위해 지하수 흐름모델과 최적화 모델을 결합한 최적전산모델을 이용하여 모의하였다. 지하수 흐름모델은 경계면모델을, 최적화 기법은 GA(Genetic Algorithm)을 이용하였다. 구축된 목적함수로는 양수정의 담수비율 최대화, 주입정에서의 수위상승량 최소화 그리고 양수개시시간 최소화로 구성하였다. 제약조건으로는 총 주입량 및 양수량 그리고 주입 및 양수정 개수이다. 서술한 목적함수와 제약조건을 만족하는 주입/양수정의 위치 및 유량을 최적전산모델로부터 얻을 수 있다. 기존 지하수 인공함양 및 개발은 사례별 연구 또는 전문가의 주관적 판단에 의존하는 경향이 있었다. 본 연구는 최적화 기법을 통해 복수의 관정에서 정량적인 산정이 가능하다. 현재 모델링에 의존한 연구로써 한계가 있지만, 추후 실제현장에 적용하여 모델 검정을 통해 신뢰도를 높이며 지하수 인공함양 개발에 많은 공헌을 할 수 있을 것으로 예상한다.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.677-687
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• 2021

The tendency to renewables is one of the consequences of changing attitudes towards energy issues. As a result, solar energy, which is the leader among renewable energies based on availability and potential, plays a crucial role in full filing global needs. Significant problems with the solar thermal power plants (STPP) are the operation time, which is limited by daylight and is approximately half of the power plants with fossil fuels, and the capital cost. Exergy analysis survey of STPP hybrid with PCM storage carried out using Engineering Equation Solver (EES) program with genetic algorithm (GA) for three different scenarios, based on eight decision variables, which led us to decrease final product cost (electricity) in optimized scenario up to 30% compare to base case scenario from 28.99 $/kWh to 20.27 $/kWh for the case study. Also, in the optimal third scenario of this plant, the inner carbon dioxide gas cycle produces 1200 kW power with a thermal efficiency of 59% and also 1000 m3/h water with an exergy efficiency of 23.4% and 79.70 kg/h with an overall exergy efficiency of 34% is produced in the tetrageneration plant.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.158-158
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• 2017

상수관망을 통한 용수 공급에서는 송수펌프, 배수지 등의 수리 시설물이 중요한 역할을 수행한다. 우리나라의 송 배수 방식은 송수펌프를 통해 고지대의 배수지에 물을 확보하고, 이를 자연유하 방식으로 공급하는 것이 일반적이며, 따라서 송 배수시스템의 운영이란 송수펌프의 가동과 그에 따른 배수지의 수위 현황을 관리하는 것을 의미한다. 이 때, 펌프의 가동을 위한 전력소모에 많은 비용이 발생되므로 효율적인 펌프 운영을 위한 최적화 연구의 필요성이 제기되었다. 기존 연구를 통해 송 배수시스템의 운영을 모의하고, 펌프 가동비용을 최소화 하는 실시간 최적 펌프운영 모형이 개발되었으나, 미리 결정된 펌프와 배수지를 바탕으로 송 배수시스템을 모의하기 때문에 계획 및 설계 단계에서 이를 활용할 수 없는 한계점이 존재하였다. 본 연구에서는 최적화 알고리즘 중 하나인 유전자 알고리즘(Genetic Algorithm, GA)을 사용하여, 실시간 펌프운영뿐만 아니라 송수펌프와 배수지의 효율적인 용량을 제시할 수 있는 최적화 모형을 개발하였다. 특히, 개발 모형은 펌프와 배수지의 설계/운영 시, 국내 설계기준, 시설물 비용, 시간별 전력단가 등을 제약조건으로 고려하여 현실적인 결과를 도출할 수 있도록 개발되었다. 본 연구는 실제 운영 중인 S시의 광역상수도 시스템을 바탕으로 개발 모형을 적용하였으며, 또한 송 배수시스템의 계획 및 관리에 활용할 수 있을 것으로 기대된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5A
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• pp.399-406
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• 2010

In this paper, the optimization of frictional bearings that applied to improve the seismic performance of conventional bridges were conducted. The nonlinear dynamic analysis of steel bridges and concrete bridges are carried out with the El Centro and artificial earthquake motions, and the reponses of the bridges were optimized by genetic algorithm. The object functions were considered with two parameters, such as shear forces and displacements at bearing, and the optimum object functions were searched by varying the weighting factors of the two parameters. As results, in case of the steel bridges, the optimum results were obtained when larger weight factor was imposed to the shear force. However, in case of concrete bridges, larger weight factor was need to the displacement for optimum results.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.34-37
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• 2007

Face recognition is still a challenging problem in pattern recognition field which is affected by different factors such as facial expression, illumination, pose etc. The facial feature such as eyes, nose, and mouth constitute a complete face. Mouth feature of face is under the undesirable effect of facial expression as many factors contribute the low performance. We proposed a new approach for face recognition under facial expression applying two cascaded classifiers to improve recognition rate. All facial expression images are treated by general purpose classifier at first stage. All rejected images (applying threshold) are used for adaptation using GA for improvement in recognition rate. We apply Gabor Wavelet as a general classifier and Gabor wavelet with Genetic Algorithm for adaptation under expression variance to solve this issue. We have designed, implemented and demonstrated our proposed approach addressing this issue. FERET face image dataset have been chosen for training and testing and we have achieved a very good success.

• Journal of KIISE:Software and Applications
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• v.32 no.4
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• pp.247-257
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• 2005

Generally, the network topology design problem is characterized as a kind of NP-hard combinatorial optimization problem, which is difficult to solve with the classical method because it has exponentially increasing complexity with the augmented network size. In this paper, we propose the efficient approach with two phase that is comprised of evolutionary computation approach based on Prufer number(PN), which can efficiently represent the spanning tree, and a heuristic method considering 2-connectivity, to solve the highly reliable network topology design problem minimizing the construction cost subject to network reliability: firstly, to find the spanning tree, genetic algorithm that is the most widely known type of evolutionary computation approach, is used; secondly, a heuristic method is employed, in order to search the optimal network topology based on the spanning tree obtained in the first Phase, considering 2-connectivity. Lastly, the performance of our approach is provided from the results of numerical examples.

• 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.

• Journal of Korean Society of Steel Construction
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• v.27 no.2
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• pp.131-142
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• 2015

In this paper, we present an optimization of truss structures subjected to stress, buckling, and natural frequency constraints. The main objective of the present study is to propose an efficient HA-SA algorithm for solving the truss optimization subject to multiple constraints. The procedure of hybrid HA-SA is a search method which a design values in harmony memory of harmony search are used as an initial value designs in simulated annealing search method. The efficient optimization of HA-SA is illustrated through several optimization examples. The examples of truss structures are used 10-Bar truss, 52-Bar truss (Dome), and 72-Bar truss for natural frequency constraints, and used 18-Bar truss and 47-Bar (Tower) truss for stress and buckling constraints. The optimum results are compared to those of different techniques. The numerical results are demonstrated the advantages of the HA-SA algorithm in truss optimization with multiple constraints.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• Computers and Concrete
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• v.32 no.2
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• pp.217-232
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• 2023

Numerous studies have been performed on the behavior of pile foundations in cold regions. This study first attempted to employ artificial neural networks (ANN) to predict pile-bearing capacity focusing on pile data recorded primarily on cold regions. As the ANN technique has disadvantages such as finding global minima or slower convergence rates, this study in the second phase deals with the development of an ANN-based predictive model improved with an Elephant herding optimizer (EHO), Dragonfly Algorithm (DA), Genetic Algorithm (GA), and Evolution Strategy (ES) methods for predicting the piles' bearing capacity. The network inputs included the pile geometrical features, pile area (m²), pile length (m), internal friction angle along the pile body and pile tip (Ø°), and effective vertical stress. The MLP model pile's output was the ultimate bearing capacity. A sensitivity analysis was performed to determine the optimum parameters to select the best predictive model. A trial-and-error technique was also used to find the optimum network architecture and the number of hidden nodes. According to the results, there is a good consistency between the pile-bearing DA-MLP-predicted capacities and the measured bearing capacities. Based on the R2 and determination coefficient as 0.90364 and 0.8643 for testing and training datasets, respectively, it is suggested that the DA-MLP model can be effectively implemented with higher reliability, efficiency, and practicability to predict the bearing capacity of piles.