• Nuclear Engineering and Technology
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• 제49권1호
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• pp.134-140
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• 2017

A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP). The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.

• Journal of Communications and Networks
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• 제9권2호
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• pp.112-117
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• 2007

In this paper, we focus on the total transmission power minimization problem for downlink beamforming multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems while ensuring each user's QoS requirement. Although the linear integer programming (LIP) solution we formulate provides the performance upper bound of the margin adaptive (MA) optimization problem, it is hard to be implemented in practice due to its high computational complexity. By regarding each user's equivalent channel gain as approximate independent values and using iterative descent method, we present a heuristic MA resource allocation algorithm. Simulation results show that the proposed algorithm efficiently converges to the local optimum, which is very close to the performance of the optimal LIP solution. Compared with existing space division multiple access (SDMA) OFDM systems with or without adaptive resource allocation, the proposed algorithm achieves significant performance improvement by exploiting the frequency diversity and multi-user diversity in downlink multiple-input single-output (MISO) OFDM systems.

• Computers and Concrete
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• 제32권1호
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• pp.1-13
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• 2023

This paper discusses a framework for predicting the flexural strength of prestressed and non-prestressed FRP reinforced T-shaped concrete beams using soft computing techniques. An analysis of 83 tests performed on T-beams of varying widths has been conducted for this purpose with different widths of compressive face, beam depth, compressive strength of concrete, area of prestressed and non-prestressed FRP bars, elasticity modulus of prestressed and non-prestressed FRP bars, and the ultimate tensile strength of prestressed and non-prestressed FRP bars. By analyzing the data using two soft computing techniques, named artificial neural networks (ANN) and gene expression programming (GEP), the fundamental parameters affecting the flexural performance of prestressed and non-prestressed FRP reinforced T-shaped beams were identified. The results showed that although the proposed ANN model outperformed the GEP model with higher values of R and lower error values, the closed-form equation of the GEP model can provide a simple way to predict the effect of input parameters on flexural strength as the output. The sensitivity analysis results revealed the most influential input parameters in ANN and GEP models are respectively the beam depth and elasticity modulus of FRP bars.

• Advances in environmental research
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• 제6권2호
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• pp.83-94
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• 2017

Air quality planning and management requires accurate and consistent records of the air quality parameters. Limited number of monitoring stations and inconsistent measurements of the air quality parameters is a very serious problem in many parts of India. It becomes difficult for the authorities to plan proactive measures with such a limited data. Estimation models can be developed using soft computing techniques considering the physics behind pollution dispersion as they can work very well with limited data. They are more realistic and can present the complete picture about the air quality. In the present case study spatio-temporal models using Linear Genetic Programming (LGP) have been developed for estimation of air quality parameters. The air quality data from four monitoring stations of an Indian city has been used and LGP models have been developed to estimate pollutant concentration of the fifth station. Three types of models are developed. In the first type, models are developed considering only the pollutant concentrations at the neighboring stations without considering the effect of distance between the stations as well the significance of the prevailing wind direction. Second type of models are distance based models based on the hypothesis that there will be atmospheric interactions between the two stations under consideration and the effect increases with decrease in the distance between the two. In third type the effect of the prevailing wind direction is also considered in choosing the input stations in wind and distance based models. Models are evaluated using Band Error and it was observed that majority of the errors are in +/-1 band.

• 디지털콘텐츠학회 논문지
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• 제17권4호
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• pp.257-263
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• 2016

케이블 드럼 스케줄은 발전소 전기설비 설계를 위한 최종단계로 레이스웨이에 포설 계획된 케이블들을 효율적으로 케이블 드럼에 할당하는 것이다. 본 논문에서는 케이블들을 코드별로 케이블 드럼 용량에 맞게 스케줄링 하는 자동화 프로그램을 구현하였으며, 케이블 드럼 스케줄을 위한 최적화 문제를 효과적으로 해결하기 위해 동적 계획 알고리즘을 적용하였다. 구현 결과 케이블 드럼 스케줄 자동화는 설계 규격대로 수행됨을 확인할 수 있었고, 기존방법에서 발생되는 케이블 부족 및 낭비와 같은 오류를 제거 및 케이블 드럼 스케줄 소요시간을 줄일 수 있었다. 발전소 전기설비를 위한 케이블은 최소 2만개 이상으로 설계되기 때문에 제안하는 자동화 프로그램을 적용한다면 심각한 오류 없이 케이블 드럼 스케줄의 설계 소요시간을 현저히 줄일 수 있을 것으로 사료된다.

• 상하수도학회지
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• 제13권1호
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• pp.95-112
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• 1999

In this research, it is proposed that optimization method is introduced and applied to the design of pipeline system in multi-regional water supply project, which has been constructed to settle the regional unbalance problems of available water resources. For the purpose, interface programs are developed to integrate linear programming model and KYPIPE model which is used for optimization and hydraulic analysis, respectively. The developed program is applied to the pipeline system design of multi-regional water supply project. The optimal diameters from the application of linear programming technique are compared with those from conventional method that is time-consuming and tedious trail and error process. Since the conventional design largely depends upon the experience of designers and the results of general hydraulic analysis, it can not be reasonable and consistent. The application of linear programming technique can make it possible to design pipeline system optimally by using same design factors of general hydraulic models. The model can select commercial discrete pipe diameter as optimal size by using pipe length as decision variables. The developed model is applied to Pohang multi-regional water supply system design with two different objective functions, which are initial construction cost and annual cost including electric cost. As results, it is calculated that the initial construction cost of 1,449,740 thousand won is saved and annual cost of 128,951 thousand won is saved for a year within study year. Also, the optimal site of pump station is selected on 5th pipe, which is located between the diverging junction to Kangdong(2) province and the diverging junction to Cheonbuk province. It is explained that pump cost is less than pipe cost in this application case study due to little pump station scale. In the case of water supply with large pump capacity, it is reasonal that the increase of pipe size is more efficient instead the increase of pump station capacity to save annual cost.

• 터널과지하공간
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• 제28권6호
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• pp.651-669
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• 2018

본 연구에서는 유전자 프로그래밍과 개체군집최적화기법을 이용하여 픽 커터의 비에너지를 예측하기 위한 모델을 제안하였다. 기계굴착장비의 굴진성능을 평가하는 것은 터널의 설계 초기 단계에서 매우 중요하며, 비에너지를 이용한 기계 굴착장비의 굴진성능평가방법은 모든 기계굴착공법에 적용될 수 있는 표준화된 방법이다. 본 연구에서는 코니컬형상의 픽 커터가 암석을 절삭할 때 요구되는 비에너지와 암석의 강도특성, 절삭조건 간의 상관관계를 분석하고자 하였으며, 선행연구를 통해 총46개의 선형절삭시험 결과를 수집하여 분석에 활용하였다. 본 연구에서 제안한 예측모델을 이용하여 산정된 픽 커터의 비에너지는 다중선형회귀분석에 비해 작은 평균제곱오차를 나타내었으며, 결정계수 또한 본 연구에서 제안한 모델이 다중선형회귀분석에 비해 우수한 예측결과를 나타내는 것을 확인할 수 있었다.

• Computers and Concrete
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• 제27권4호
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• pp.319-332
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• 2021

The performance of gene expression programming (GEP) in predicting the compressive strength of bacteria-incorporated geopolymer concrete (GPC) was examined in this study. Ground-granulated blast-furnace slag (GGBS), new bacterial strains, fly ash (FA), silica fume (SF), metakaolin (MK), and manufactured sand were used as ingredients in the concrete mixture. For the geopolymer preparation, an 8 M sodium hydroxide (NaOH) solution was used, and the ambient curing temperature (28℃) was maintained for all mixtures. The ratio of sodium silicate (Na2SiO3) to NaOH was 2.33, and the ratio of alkaline liquid to binder was 0.35. Based on experimental data collected from the literature, an evolutionary-based algorithm (GEP) was proposed to develop new predictive models for estimating the compressive strength of GPC containing bacteria. Data were classified into training and testing sets to obtain a closed-form solution using GEP. Independent variables for the model were the constituent materials of GPC, such as FA, MK, SF, and Bacillus bacteria. A total of six GEP formulations were developed for predicting the compressive strength of bacteria-incorporated GPC obtained at 1, 3, 7, 28, 56, and 90 days of curing. 80% and 20% of the data were used for training and testing the models, respectively. R2 values in the range of 0.9747 and 0.9950 (including train and test dataset) were obtained for the concrete samples, which showed that GEP can be used to predict the compressive strength of GPC containing bacteria with minimal error. Moreover, the GEP models were in good agreement with the experimental datasets and were robust and reliable. The models developed could serve as a tool for concrete constructors using geopolymers within the framework of this research.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.

• 한국컴퓨터정보학회논문지
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• 제17권10호
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• pp.217-226
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• 2012

이 연구에서는 비주얼베이직, 스크래치, 교육용 로봇의 한 종류인 피코 크리켓을 활용한 초등학교 프로그래밍 수업의 과정적인 모습을 과학적으로 분석하고자 하였다. 인지적인 영역의 분석은 창의성 검사로 했으며, 수업의 과정적인 분석은 최근 수업 장학에 널리 사용되고 있는 학생 과업 집중도와 학생 활동 소요 변인 분석법을 활용하였다. 분석 결과 창의성에서는 세 교구를 활용한 집단 모두 수업 전보다 향상은 있었지만, 유의미한 향상은 아닌 것으로 분석되었다. 학생 과업집중 분석 결과의 수업 시점에 따른 결과를 살펴보면 피코크리켓 활용 집단과 스크래치 활용집단은 약간의 하락을 나타냈으나, 비주얼베이직 활용 집단의 과업 집중도가 현저하게 떨어지는 것으로 나타났다. 마지막으로 학생 활동 소요 변인 분석 결과는 스크래치와 피코 크리켓 활용 집단에서 토론 토의, 프로그래밍에 비교적 많은 시간이 할애된 반면 비주얼베이직 활용 집단의 경우 코딩 에러 수정에 많은 시간이 할애 되었다. 다만 피코크리켓 활용 집단에서는 교구의 준비와 기기 점검 등의 준비 활동에 많은 시간이 할애됨을 알 수 있어 수업 설계시 반영해야 할 사항으로 분석되었다. 이를 통해 초등학교 프로그래밍 교육에서는 비주얼베이직 언어보다는 스크래치와 같은 교육용 프로그래밍 언어와 교육용 로봇을 적절히 활용하는 것이 학생들의 학습 집중도와 수업 시간 운영에 효과적인 대안이 될 수 있을 것으로 판단되었다.