• Computers and Concrete
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• 제31권4호
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• pp.315-325
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• 2023

Calcium leaching is one of the main deterioration factors in concrete structures contact with water, such as dams, water treatment structures, and radioactive waste structures. It causes a porous microstructure and may be coupled with various harmful factors resulting in mechanical degradation of concrete. Several numerical modeling studies focused on the calcium leaching depth prediction. However, these required a lot of cost and time for many experiments and analyses. This study presents an artificial neural network (ANN) approach to predict the leaching depth quickly and accurately. Totally 132 experimental data are collected for model training and validation. An optimal ANN model was proposed by ANN topology. Results indicate that the model can be applied to estimate the calcium leaching depth, showing the determination coefficient of 0.91. It might be used as a simulation tool for engineering problems focused on durability.

• 소성∙가공
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• 제29권5호
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• pp.272-281
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• 2020

As the thickness of mobile communication devices is getting thinner, the size of the internal parts is also getting smaller. Among them, the battery case requires a high-level deep drawing technique because it has a rectangular shape with a large aspect ratio. In this study, the initial blank shape was optimized to minimize earing in a multi-stage deep drawing process using an artificial neural network(ANN). There has been no reported case of applying artificial neural network technology to the initial blank optimal design for a square case with large aspect ratio. The training data for ANN were obtained though simulation, and the model reliability was verified by performing comparative study with regression model using random sample test and goodness-of-fit test. Finally, the optimal design of the initial blank shape was performed through the verified ANN model.

• 전력전자학회논문지
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• 제11권1호
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• pp.29-37
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• 2006

본 논문에서는 LM-FNN(learning Mechanism-Fuzzy Neural Network) 제어기를 이용하여 IPMSM 드라이브의 고성능 속도를 제어한다. 고성능제어를 위하여 신경회로망과 퍼지제어를 혼합 적용한 FNN을 설계한고 더욱 성능을 개선하기 위하여 학습 메카니즘을 이용하여 FNN 제어기의 파라미터를 갱신시킨다. 그리고 ANN(Artificial Neural Network)을 이용하여 IPMSM 드라이브의 속도 추정기법을 제시한다. 추정속도의 타당성을 입증하기 위하여 시스템을 구성하여 제어특성을 분석한다. 그리고 추정된 속도를 지령속도와 비교하여 전류제어와 공간벡터 PWM을 통하여 IPMSM의 속도를 제어한다. 본 연구에서 제시한 LM-FNN과 ANN 제어기의 제어특성과 추정성능을 분석하고 그 결과를 제시한다.

• Journal of Electrical Engineering and Technology
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• 제10권3호
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• pp.877-887
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• 2015

Rapid development of cities with constant increasing load and deregulation in electricity market had forced the transmission lines to operate near their threshold capacity and can easily lead to voltage instability and caused system breakdown. To prevent such catastrophe from happening, accurate readings of voltage stability condition is required so that preventive equipment and operators can execute security procedures to restore system condition to normal. This paper introduced Enhanced Hybrid Particle Swarm Optimization algorithm to estimate the voltage stability condition which utilized Fast Voltage Stability Index (FVSI) to indicate how far or close is the power system network to the collapse point when the reactive load in the system increases because reactive load gives the highest impact to the stability of the system as it varies. Particle Swarm Optimization (PSO) had been combined with the ANN to form the Enhanced Hybrid PSO-ANN (EHPSO-ANN) algorithm that worked accurately as a prediction algorithm. The proposed algorithm reduced serious local minima convergence of ANN but also maintaining the fast convergence speed of PSO. The results show that the hybrid algorithm has greater prediction accuracy than those comparing algorithms. High generalization ability was found in the proposed algorithm.

• 한국농공학회논문집
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• 제59권3호
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• pp.97-110
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• 2017

This study investigates the efficiencies of machine learning models, including artificial neural network (ANN), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF), for reservoir water level forecasting in the Chungju Dam, South Korea. The models' efficiencies are assessed based on model efficiency indices and graphical comparison. The forecasting results of the models are dependent on lead times and the combination of input variables. For lead time t = 1 day, ANFIS1 and ANN6 models yield superior forecasting results to RF6 and GRNN6 models. For lead time t = 5 days, ANN1 and RF6 models produce better forecasting results than ANFIS1 and GRNN3 models. For lead time t = 10 days, ANN3 and RF1 models perform better than ANFIS3 and GRNN3 models. It is found that ANN model yields the best performance for all lead times, in terms of model efficiency and graphical comparison. These results indicate that the optimal combination of input variables and forecasting models depending on lead times should be applied in reservoir water level forecasting, instead of the single combination of input variables and forecasting models for all lead times.

• Journal of Electrical Engineering and Technology
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• 제3권2호
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• pp.167-176
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• 2008

Vulnerability assessment of power systems is important so as to determine their ability to continue to provide service in case of any unforeseen catastrophic contingency such as power system component failures, communication system failures, human operator error, and natural calamity. An approach towards the development of on-line power system vulnerability assessment is by means of using an artificial neural network(ANN), which is being used successfully in many areas of power systems because of its ability to handle the fusion of multiple sources of data and information. An important consideration when applying ANN in power system vulnerability assessment is the proper selection and dimension reduction of training features. This paper aims to investigate the effect of using various feature extraction methods on the performance of ANN as well as to evaluate and compare the efficiency of the proposed feature extraction method named as neural network weight extraction. For assessing vulnerability of power systems, a vulnerability index based on power system loss is used and considered as the ANN output. To illustrate the effectiveness of ANN considering various feature extraction methods for vulnerability assessment on a large sized power system, it is verified on the IEEE 300-bus test system.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2006년도 총회 및 춘계학술발표회
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• pp.391-393
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• 2006

In this study, the performance of artificial neural network (ANN) models for estimating spatial distribution of water quality was evaluated using electric conductivity (EC) values in landfill site. For the ANN model development, feedforward neural networks and backpropagation algorithm with gradient descent method were used. In Test 1, the interpolation ability of the ANN model was evaluated. Results of the ANN model were more precise than those of the Kriging model. In Test 2, spatial distributions of EC values were predicted using precipitation data. Results seemed to be reasonable, however, they showed a limitation of ANN models in extrapolations.

• Smart Structures and Systems
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• 제25권2호
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• pp.183-195
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• 2020

Mineral admixtures have been widely used to produce concrete. Pozzolans have been utilized as partially replacement for Portland cement or blended cement in concrete based on the materials' properties and the concrete's desired effects. Several environmental problems associated with producing cement have led to partial replacement of cement with other pozzolans. Furnace slag and fly ash are two of the pozzolans which can be appropriately used as partial replacements for cement in concrete. However, replacing cement with these materials results in significant changes in the mechanical properties of concrete, more specifically, compressive strength. This paper aims to intelligently predict the compressive strength of concretes incorporating furnace slag and fly ash as partial replacements for cement. For this purpose, a database containing 1030 data sets with nine inputs (concrete mix design and age of concrete) and one output (the compressive strength) was collected. Instead of absolute values of inputs, their proportions were used. A hybrid artificial neural network-genetic algorithm (ANN-GA) was employed as a novel approach to conducting the study. The performance of the ANN-GA model is evaluated by another artificial neural network (ANN), which was developed and tuned via a conventional backpropagation (BP) algorithm. Results showed that not only an ANN-GA model can be developed and appropriately used for the compressive strength prediction of concrete but also it can lead to superior results in comparison with an ANN-BP model.

• 디지털융복합연구
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• 제18권8호
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• pp.231-242
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• 2020

연구의 목적은 가정에서 안정 시 인체의 생리적 활력 정보를 센서와 ICT 정보 기술을 통해 연속적으로 수집하는 시스템과 수집된 정보를 이용하여 당뇨병증 유무를 예측하는 인공신경망 기계학습 방법과 필수적인 기본 변수 값을 제시하였다. 연구 방법은 정상인(DM-) 20명과 당뇨병(DM+) 15명을 대상으로 BCG와 ECG 센서의 심박수 측정값의 상관 관계를 분석하였으며 상관 계수는 R²=0.959이다. Artificial Neural Network(ANN) 기계학습 프로그램을 이용하여 당뇨병증 예측 가능성을 확인하였고 입력 변수는 심박변이도의 시계열정보와 심박수, 심박변이도, 호흡율, 박동량 정보, 최저혈압, 최고혈압, 년령, 성별이며 ANN 기계학습 예측 정확도는 99.53%이다. 그리고 향후 ANN 기계학습 방법을 활용하여 BMI 정보를 이용한 당뇨예측 모델, 심장 기능 장애 예측 모델, 수면장애 분석 모델 등의 계속적인 연구가 필요하다.

• 한국경영과학회지
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• 제26권4호
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• pp.55-70
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• 2001

Artificial neural network(ANN) models have been widely used for the classification problems in business such as bankruptcy prediction, credit evaluation, etc. Although the application of ANN to classification of consumers＇ choice behavior is a promising research area, there have been only a few researches. In general, most of the researches have reported that the classification performance of the ANN models were better than conventional statistical model Because the survey data on consumer behavior may include much noise and missing data, ANN model will be more robust than conventional statistical models welch need various assumptions. The purpose of this paper is to study the potential of the ANN model for forecasting consumers＇ choice behavior based on survey data. The data was collected by questionnaires to the shoppers of department stores and discount stores. Then the correct classification rates of the ANN models for the training and test sample with that of multiple discriminant analysis(MDA) and logistic regression(Logit) model. The performance of the ANN models were betted than the performance of the MDA and Logit model with respect to correct classification rate. By using input variables identified as significant in the stepwise MDA, the performance of the ANN models were improved.