• 대한토목학회논문집
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• 제34권6호
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• pp.1779-1786
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• 2014

물리현상의 난해성으로 인해 수학적인 관계식이 제시되기 어려운 경우 인공지능 기술에 근거한 다양한 기법이 적용되어 왔다. 수리학 분야의 대표적인 예로 교각주위 국부세굴 문제를 들 수 있다. 본 연구에서는 유전자 알고리즘의 진화된 방법인 GEP 기법을 이용하여 교각주위 국부세굴을 예측하는 방법을 제시하였다. 64개의 실험 자료를 이용하여 GEP 모형을 학습시켜 회귀식을 구축하였으며, 33개의 실험 자료를 이용하여 구축된 모형의 검증을 실시하였다. 평형세굴심 예측을 위하여 차원을 갖는 일반 변수와 표준화된 변수로 GEP 모형을 구축하여 예측 결과를 비교하였는데, 차원을 갖는 변수에 의한 GEP 모형이 세굴심을 더 잘 예측하는 것으로 나타났다. 구축된 GEP 모형을 두 가지 현장 실측자료에 적용하였다. 적용 결과, 실험 자료에 적용한 경우에 비해 예측의 정확도가 낮아지는 것을 확인하였다. 또한, 현장 실측자료를 이용하여 학습시킨 경우 실험 자료를 이용하는 경우 보다 예측 능력이 많이 향상되는 것으로 나타났다. GEP 모형의 적용성을 위해 ANN 모형과의 비교를 수행하였으며, 본 연구에서 사용된 GEP 모형이 교각주위 국부세굴 예측에 대하여 실내 및 현장 모두 ANN 모형보다 우수한 것으로 나타났다.

• Bulletin of the Korean Chemical Society
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• 제31권4호
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• pp.808-814
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• 2010

Activities of nanostructure HZSM-5 and Co-ZSM-5 catalysts (with different Co-loading) for catalytic conversion of ethyl acetate and toluene were studied. The catalysts were prepared by wet impregnation method and were characterized by XRD, BET, SEM, TEM and ICP-AES techniques. Catalytic studies were carried out inside a U-shaped fixed bed reactor under atmospheric pressure and different temperatures. Toluene showed lower reactivity than ethyl acetate for conversion on Co-ZSM-5 catalysts. The effect of Co loading on conversion was prominent at temperatures below $400^{\circ}C$ and $450^{\circ}C$ for ethyl acetate and toluene respectively. In a binary mixture of organic compounds, toluene and ethyl acetate showed an inhibition and promotional behaviors respectively, in which the conversion of toluene was decreased at temperatures above $350^{\circ}C$. Inhibition effect of water vapor was negligible at temperatures above $400^{\circ}C$. An artificial neural networks model was developed to predict the conversion efficiency of ethyl acetate on Co-ZSM-5 catalysts based on experimental data. Predicted results showed a good agreement with experimental results. ANN modeling predicted the order of studied variable effects on ethyl acetate conversion, which was as follows: reaction temperature (50%) > ethyl acetate inlet concentration (25.085%) > content of Co loading (24.915%).

• 한국습지학회지
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• 제15권1호
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• pp.115-125
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• 2013

본 연구에서는 강수의 공간적 편차가 큰 산악지역에서 축소기법을 적용하기 위한 방법론을 마련하고 이를 이용하여 미래 강수특성의 변화를 추정하고자 하였다. 이를 위하여 한반도내 산악지역이라고 할 수 있는 남한강유역을 대상유역으로 선정하였고 일반적인 축소기법 중의 하나인 신경망과 고도자료를 부가자료로 활용하여 유역의 지형적 특성을 반영할 수 있는 SKlm 기법을 연계하여 신경망-SKlm 모형(ANN-SKlm : Artificial Neural Network - Simple Kriging with varying local means)을 구축하였다. 유역내 6개의 기상관측소 지점의 월강수량을 이용하여 신경망-SKlm 기법과 기존 강수량의 공간분포 방법인 Thiessen 및 Ordinary Kriging 을 적용하여 비교 평가하였다. 유역내에 보다 밀도있게 구성되어 있는 25개 강우관측소 지점을 대상으로 각 기법을 평가한 결과 고도자료를 부가자료로 사용하는 SKlm 기법이 가장 우수한 결과를 나타내었다.

• Advances in nano research
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• 제12권6호
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• pp.549-566
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• 2022

Mining of ore minerals (sfalerite, cinnabar, and chalcopyrite) from the old mine has led in significant environmental effects as contamination of soils and plants and acidification of water. Also, nanoparticles (NP) have obtained global importance because of their widespread usage in daily life, unique properties, and rapid development in the field of nanotechnology. Regarding their usage in various fields, it is suggested that soil is the final environmental sink for NPs. Nanoparticles with excessive reactivity and deliverability may be carried out as amendments to enhance soil quality, mitigate soil contaminations, make certain secure land-software of the traditional change substances and enhance soil erosion control. Meanwhile, there's no record on the usage of Nano superior substances for mine soil reclamation. In this study, five soil specimens have been tested at 4 sites inside the region of mine (<100 m) to study zeolites, and iron sulfide nanoparticles. Also, through using Artificial Neural Network (ANN) and Extreme Learning Machine (ELM), this study has tried to appropriately estimate the mechanical properties of soil under the effect of these Nano particles. Considering the RMSE and R2 values, Zeolite Nano materials could enhance the mine soil fine through increasing the clay-silt fractions, increasing the water holding capacity, removing toxins and improving nutrient levels. Also, adding iron sulfide minerals to the soils would possibly exacerbate the soil acidity problems at a mining site.

• Steel and Composite Structures
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• 제41권5호
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• pp.761-773
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• 2021

This study presents a 3D non-linear finite element (FE) assessment of dynamic soil-structure interaction (SSI). The numerical investigation has been performed on the time domain through a Finite Element (FE) system, while considering the nonlinear behavior of soil and the multi-directional nature of genuine seismic events. Later, the FE outcomes are analyzed to the recorded in-situ free-field and structural movements, emphasizing the numerical model's great result in duplicating the observed response. In this work, the soil response is simulated using an isotropic hardening elastic-plastic hysteretic model utilizing HSsmall. It is feasible to define the non-linear cycle response from small to large strain amplitudes through this model as well as for the shift in beginning stiffness with depth that happens during cyclic loading. One of the most difficult and unexpected tasks in resolving soil-structure interaction concerns is picking an appropriate ground motion predicted across an earthquake or assessing the geometrical abnormalities in the soil waves. Furthermore, an artificial neural network (ANN) has been utilized to properly forecast the non-linear behavior of soil and its multi-directional character, which demonstrated the accuracy of the ANN based on the RMSE and R² values. The total result of this research demonstrates that complicated dynamic soil-structure interaction processes may be addressed directly by passing the significant simplifications of well-established substructure techniques.

• Computers and Concrete
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• 제34권1호
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• pp.93-122
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• 2024

One well-known reason for using Fiber Reinforced Polymer (FRP) composites is to improve concrete strength and strain capacity via external confinement. Hence, various studies have been undertaken to offer a good illustration of the response of FRP-wrapped concrete for practical design intents. However, in such studies, the strength and strain of the confined concrete were predicted using regression analysis based on a limited number of test data. This study presents an approach based on artificial neural networks (ANNs) to develop models to predict the strength and strain at maximum stress enhancement of circular concrete cross-sections confined with different FRP types (Carbone, Glass, Aramid). To achieve this goal, a large test database comprising 493 axial compression experiments on FRP-confined concrete samples was compiled based on an extensive review of the published literature and used to validate the predicted artificial intelligence techniques. The ANN approach is currently thought to be the preferred learning technique because of its strong prediction effectiveness, interpretability, adaptability, and generalization. The accuracy of the developed ANN model for predicting the behavior of FRP-confined concrete is commensurate with the experimental database compiled from published literature. Statistical measures values, which indicate a better fit, were observed in all of the ANN models. Therefore, compared to existing models, it should be highlighted that the newly developed models based on FRP type are remarkably accurate.

• 한국구조물진단유지관리공학회 논문집
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• 제11권1호
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• pp.103-112
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• 2007

구조 재료와 시공기술의 발달로 구조물은 높고 길게 설계할 수 있게 되었으나, 그에 따른 진동문제와 사용성에 관한 문제가 발생하였고, 구조물의 과다한 변위는 구조물에 심각한 손상을 발생시켰다. 이러한 구조물의 진동 문제를 해결하기 위하여 본 논문에서는 구조물의 상태벡터와 제어력만으로 구성된 훈련패턴을 기본으로 하여 인공신경망이론과 확률신경망이론을 사용하여 구조물의 진동을 능동제어하는 방법을 제안하였다. 구조물의 제어를 위해 LQR 제어알고리즘을 이용하여 구조물의 상태벡터와 제어력을 구한 후, 상태벡터를 입력으로 제어력을 출력으로 하는 인공신경망과 확률신경망의 훈련패턴을 구성하였다. 제안된 방법을 사용하여 Northridge 지진하중을 받는 3층 빌딩구조물을 제어하였고, 제안된 인공신경망과 확률신경망의 제어 결과를 비교하였다.

• Computers and Concrete
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• 제34권2호
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• pp.151-168
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• 2024

This research aimed to appraise the effectiveness of four optimization approaches - cuckoo optimization algorithm (COA), multi-verse optimization (MVO), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) - that were enhanced with an artificial neural network (ANN) in predicting the bearing capacity of shallow foundations located on cohesionless soils. The study utilized a database of 97 laboratory experiments, with 68 experiments for training data sets and 29 for testing data sets. The ANN algorithms were optimized by adjusting various variables, such as population size and number of neurons in each hidden layer, through trial-and-error techniques. Input parameters used for analysis included width, depth, geometry, unit weight, and angle of shearing resistance. After performing sensitivity analysis, it was determined that the optimized architecture for the ANN structure was 5×5×1. The study found that all four models demonstrated exceptional prediction performance: COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP. It is worth noting that the MVO-MLP model exhibited superior accuracy in generating network outputs for predicting measured values compared to the other models. The training data sets showed R² and RMSE values of (0.07184 and 0.9819), (0.04536 and 0.9928), (0.09194 and 0.9702), and (0.04714 and 0.9923) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively. Similarly, the testing data sets produced R² and RMSE values of (0.08126 and 0.07218), (0.07218 and 0.9814), (0.10827 and 0.95764), and (0.09886 and 0.96481) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively.

• 한국멀티미디어학회논문지
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• 제23권7호
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• pp.870-882
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• 2020

It is that failure of equipment at the factory site causes personal injury and property damage. We are required a real-time monitoring and risk forecasting techniques to prevent for equipment failure. In this paper, we proposed a 3-phase smart plug and real-time monitoring system that can be used in factories, and collected environmental information and power information using a smart plug to analyze the data. In order to analyze the correlation between the risk situation and the collected data, we predicted the risk situation using Linear Regression, SVM, and ANN algorithms. As a result, the SVM and ANN algorithms obtained high predictive accuracy and developed a mobile app that could use it to check the risk forecast results.

• Environmental Analysis Health and Toxicology
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• 제19권2호
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• pp.161-167
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• 2004

Air quality monitoring is a primary activity for industrial and social environment. The government identifies the pollutants that each industry must monitor. Especially, the VOCs (Volatile Organic Compounds), which are very harmful to human body and environment atmosphere, should be controlled under the government policy. However, the VOCs, which have not been confirmed in emission sources are very difficult to monitor. It is needed to develop the monitoring system that allow the continuous and in situ measurement of VOCs mixture in different environmental matrices. Gas chromatography and mass spectrometry are the most prevalent current techniques among those available for the analysis of VOCs. But, they need a large size analytical instrument, which costs a great deal for purchase and operation. In addition, it has some limitations for realtime environmental monitoring such as location problems and slow processing time. Recently, several companies have commercialized a portable VOCs measurement systems, which cannot classify various kinds of VOCs but total quantities. We have developed a VOCs measurement system, which recognizes various kinds and quantities of VOCs, such as benzene, toluene, and xylene (BTX). Also, it can be used as a stand- alone type and/or fixed type in the vehicle with rack for real -time environmental monitoring.