• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.169-186
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• 2018

The social influence of the elderly population will accelerate in a rapidly aging society. The purpose of this study is to establish a methodology for extracting vulnerable districts of the welfare of the aged through machine learning(ML), artificial neural network(ANN) and geospatial analysis. In order to establish the direction of analysis, this progressed after an interview with volunteers who over 65-year old people, public officer and the manager of the aged welfare facility. The indicators are the geographic distance capacity, elderly welfare enjoyment, officially assessed land price and mobile communication based on old people activities where 500 m vector areal unit within 15 minutes in Yongin-city, Gyeonggi-do. As a result, the prediction accuracy of 83.2% in the support vector machine(SVM) of ML using the RBF kernel algorithm was obtained in simulation. Furthermore, the correlation result(0.63) was derived from ANN using backpropagation algorithm. A geographically weighted regression(GWR) was also performed to analyze spatial autocorrelation within variables. As a result of this analysis, the coefficient of determination was 70.1%, which showed good explanatory power. Moran's I and Getis-Ord Gi coefficients are analyzed to investigate spatially outlier as well as distribution patterns. This study can be used to solve the welfare imbalance of the aged considering the local conditions of the government recently.

• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.73-82
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• 2019

Recently, as the occurrence frequency of sudden floods due to climate change increased, the flood damage on riverside social infrastructures was extended so that there has been a threat of overflow. Therefore, a rapid prediction of potential flooding in riverside social infrastructure is necessary for administrators. However, most current flood forecasting models including hydraulic model have limitations which are the high accuracy of numerical results but longer simulation time. To alleviate such limitation, data driven models using artificial neural network have been widely used. However, there is a limitation that the existing models can not consider the time-series parameters. In this study the water surface elevation of the Hangang River bridge was predicted using the NARX model considering the time-series parameter. And the results of the ANN and RNN models are compared with the NARX model to determine the suitability of NARX model. Using the 10-year hydrological data from 2009 to 2018, 70% of the hydrological data were used for learning and 15% was used for testing and evaluation respectively. As a result of predicting the water surface elevation after 3 hours from the Hangang River bridge in 2018, the ANN, RNN and NARX models for RMSE were 0.20 m, 0.11 m, and 0.09 m, respectively, and 0.12 m, 0.06 m, and 0.05 m for MAE, and 1.56 m, 0.55 m and 0.10 m for peak errors respectively. By analyzing the error of the prediction results considering the time-series parameters, the NARX model is most suitable for predicting water surface elevation. This is because the NARX model can learn the trend of the time series data and also can derive the accurate prediction value even in the high water surface elevation prediction by using the hyperbolic tangent and Rectified Linear Unit function as an activation function. However, the NARX model has a limit to generate a vanishing gradient as the sequence length becomes longer. In the future, the accuracy of the water surface elevation prediction will be examined by using the LSTM model.

• Journal of the Korean Chemical Society
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• v.45 no.5
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• pp.422-428
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• 2001

Retention relative times(RRTs) of PAH molecules and their derivatives in gas chromatography are trained and predicted in testing sets using a multiple linear regression(MLR) and an artificial neural network(ANN). The main descriptors of PAHs and their derivatives in QSRR are the square root of molecular weight(sqmw), molecular connectivity($^1{\chi}_v$), molecular dipole moment(D) and length-to-breadth ratios(L/B). The results of MLR shows that a heavy molecule has a propensity for long retention time. L/B closely related with slot model is a good descriptor in MLR. On the other hand, ANN which is not effected by the linear dependencies among the descriptors were exclusively based on molecular weight and molecular dipole moment. The variances which shows the accuracy of prediction for retention times in testing sets are 1.860, 0.206 for MLR and ANN, respectively. It was shown that ANN can exceed the MLR in prediction accuracy.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.11 no.3
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• pp.28-38
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• 2008

The purpose of this study are to compare existing presence-absence predictive models and to predict suitable habitat for Goral (Nemorhaedus caudatus raddeanus) that is an endangered and protected species in Seoraksan national park using the best model among existing predictive models. The methods of this study are as follows. First, 375 location data and 9 environmental data layers were implemented to build a model. Secondly, 4 existing presence-absence models : Generalized Linear Model (GLM), Generalized Addictive Model (GAM), Classification and Regression Tree (CART), and Artificial Neural Network (ANN) were tested to predict the Goal habitat. Thirdly, ROC (Receiver Operating Characteristic) and Kappa statistics were used to calculate a model performance. Lastly, we verified models and created habitat suitability maps. The ROC AUC (Area Under the Curve) and Kappa values were 0.697/0.266 (GLM), 0.729/0.313 (GAM), 0.776/0.453 (CART), and 0.858/0.559 (ANN). Therefore, ANN was selected as the best model among 4 models. The models showed that elevation, slope, and distance to stream were the significant factors for Goal habitat. The ratio of predicted area of ANN using a threshold was 31.29%, but the area decreased when human effect was considered. We need to investigate the difference of various models to build a suitable wildlife habitat model under a given condition.

• Journal of Ocean Engineering and Technology
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• v.18 no.6 s.61
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• pp.79-83
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• 2004

The curved blocks which compose the bow and stem of a ship contain many skewed joints that are inclined horizontally and vertically. Most of these joints have a large fitness error and are continuously changing their form and are not easily accessible. The welding position and parameter values should be appropriately set in correspondence to the shape and the inclination of the joints. The welding parameters such as current, voltage, travel speed, and melting rate, are related to each other and their values must be in a specific limited range for the sound welding. These correlations and the ranges are dependent up on the kind and size of wire, shielding gas, joint shape and fitness. To determine these relationships, extensive welding experiments were performed. The experimental data were processed using several information processing technologies. The regression method was used to determine the relationship between current voltage, and deposition rate. When a joint is inclined, the weld bead should be confined to a the limited size, inorder to avoid undercut as well as overlap due to flowing down of molten metal by gravity. The dependency of the limited weld size which is defined as the critical deposited area on various factors such as the horizontally and vertically inclined angle of the joint, skewed angle of the joint, up or down welding direction and weaving was investigated through a number of welding experiments. On the basis of this result, an ANN system was developed to estimate the critical deposited area. The ANN system consists of a 4 layer structure and uses an error back propagation learning algorithm. The estimated values of the ANN were validated using experimental values.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.159-159
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• 2016

북한 지역에서 시작되어 한반도의 금문댐까지 연결되는 임진강은 북한지역의 유출량 미계측으로 인해 유출량 산출에 많은 어려움이 있어왔다. 본 연구에서는 위성 데이터를 활용하여 미계측 유역의 유출량을 추정 할 수 있는 기법을 제시하였다. Satellite-derived Flow Signal (SDF)는 위성 기반 특정 지역의 유출 정보를 제공하며, JAXA의 GCOM-W1 위성에 탑재된 Advanced Microwave Scanning Radiometer 2(AMSR2) 센서에서 산출된다. 본 연구에서는 SDS 뿐 아니라 유출에 크게 관련이 있는 지표 토양수분 데이터와 식생인자를 임진강 유출 값을 예측하기 위한 입력 값으로 활용하였다. 토양수분 데이터는 Metop-A 위성에 탑재된 Advanced Scatterometer(ASCAT) 센서에서 산출되는 데이터를 활용하였으며, 식생데이터는 Aqua 위성에 탑재된 Moderate Resolution Imaging Spectroradiometer(MODIS) 센서에서 측정되는 Normalized Difference Vegetation Index(NDVI) 데이터를 활용하였다. 추가적으로 SDS, 토양수분, NDVI 데이터는 다양한 lag time으로 약 150여개의 입력데이터로 세분화되었다. 150개의 방대한 입력인자는 Partial Mutual Information(PMI) 방법을 통해 소수 중요 인자들로 간추려져 기계 학습 입력인자로 활용되었다. 기계학습에 있어서는 Support Vector Machine(SVM), Artificial Neural Network (ANN) 기법을 활용하였다. SVM, ANN을 통해 모델화된 유출데이터는 금문댐 유출데이터와 비교/분석되었다. SVM 기법 기반의 유출량은 실제 유출량과 0.73의 상관계수를 보여주었고, ANN 기법 기반의 유출량은 0.66의 상관계수를 결과를 나타내었다. 하지만 SVM 기반 유출데이터는 과소 산정 되는 경향을 보였으며, ANN 기법 기반의 유출량은 과대산정되는 결과가 산출되는 한계점이 있음을 파악할 수 있었다.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.1
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• pp.1-9
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• 2019

In electric agricultural machine the gearbox is used to increase torque and lower the output speed of the motor shaft. The gearbox consists of several shafts, helical gears and spur gears works in series. Optimization plays an important role in gear design as reducing the weight or volume of a gear set will increase its service life and improve the bearing capacity. In this paper the basic design parameters for gear like shaft diameter and face width are considered as the input variables. The bending stress and material volume is considered as the objective function. ANSYS was used to investigate the bending stress when the variable was changed. Artificial Neural Network (ANN) was used to obtain the mathematical model of the system based on the bending stress behaviour. The ANN was used since the output system is nonlinear. The Genetic Algorithm (GA)　technique of optimization is used to obtain the optimized values of shaft diameter and face width on the pinion based on the ANN mathematical model and the results are compared as that obtained using the traditional method. The ANN and GA were performed using MATLAB. The simulation results were shown that the proposed algorithm was successfully calculated the value of shaft diameter and face width to obtain the minimal bending stress and material volume of the gearbox.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.70-70
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• 2023

강우 발생 중 용담댐 상류로부터 용담댐으로 유입되는 유입량을 정확하게 예측하는 것은 하류 지역의 홍수 피해를 최소화하기 위한 댐의 적절한 운영에 필수적이다. 물리 기반 강우-유출 시뮬레이션 모형은 물리적 과정의 이해를 바탕으로 홍수 예측 분야에 광범위하게 사용되고 있다. 그러나 복잡한 물리 과정을 완벽히 이해하는 것은 거의 불가능하므로 다양한 가정 조건들을 이용해 복잡한 과정을 단순화하여 계산해야 하는 한계가 존재한다. 최근에는 방대한 데이터의 축적과 컴퓨터 능력의 향상으로 인해 데이터 기반 모형이 다양한 실무 문제를 해결하는 데 강력한 도구로 활용되고 있을 뿐 아니라 시뮬레이션 및 예측 등에도 다양하게 이용되고 있다. 그러나 예측 시간이 늘어날수록 입력자료로 이용되는 과거 자료와 출력자료로 이용되는 미래자료와의 상관관계가 줄어들어 모형의 성능이 저하된다. 따라서 본 연구에서는 용담댐의 시간당 유입량을 예측하기 위해 물리 기반 강우-유출 모형과 오차 보정 모형을 결합한 하이브리드 접근 방식을 제안한다. 물리 기반 강우-유출 모형으로는 HEC-HMS 모형을 사용하였으며, 오차 보정 모형에는 기계학습 모형인 인공신경망(Artificial Neural Network, ANN) 모형을 사용하였다. HEC-HMS 모형, ANN 및 하이브리드 모형(HEC-HMS + ANN)의 성능을 비교하기 위해 20 개의 홍수 사상을 모형 구축 및 검증에 사용하였다. 그 결과 하이브리드 모형은 예측 시간이 늘어날수록 HEC-HMS 및 ANN 모형보다 우수한 성능을 나타냈다. 물리모형에 기계학습을 이용한 오차 보정 절차를 통합한 경우 홍수 유출 예측의 정확성이 향상되었다. 다양한 모형의 비교 결과 본 연구에서 적용한 하이브리드 모형이 물리기반 강우-유출 모형 및 순수 기계학습 모형보다 우수한 성능을 보여줌으로써, 하이브리드 모형은 물리모형과 순수 기계학습 모형의 단점들을 보완하는데 이용할 수 있음을 나타낸다. 이 연구의 주요 목적은 강우-유출 시물레이션 모형의 오차 보정 기술에 대한 더 깊은 이해를 제공하는데 있다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2B
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• pp.155-163
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• 2011

In this study, a soil moisture estimation model was developed using a decision tree model, an artificial neural networks (ANN) model, remotely sensed data, and ground network data of daily precipitation, soil moisture and surface temperature. Soil moisture data of the Yongdam dam basin (5 sites) were used for model validation. Satellite remote sensing data and geographical data and meteorological data were used in the classification and regression tree (CART) model for data classification and the ANNs model was applied for clustered data to estimate soil moisture. Soil moisture data of Jucheon, Bugui, Sangjeon, Ahncheon sites were used for training and the correlation coefficient between soil moisture estimates and observations was between 0.92 to 0.96, root mean square error was between 1.00 to 1.88%, and mean absolute error was between 0.75 to 1.45%. Cheoncheon2 site was used for validation. Test statistics showed that the correlation coefficient, the root mean square error, the mean absolute error were 0.91, 3.19%, and 2.72% respectively. Results demonstrated that the developed soil moisture model using CART and ANN was able to apply for the estimation of soil moisture distribution.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.3
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• pp.18-25
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• 2020

The unconfined compressive strength of lightweight treated soils strongly depends on mixing ratio. To characterize the relation between various LTS components and the unconfined compressive strength of LTS, extensive studies have been conducted, proposing normalized factor using regression models based on their experimental results. However, these results obtained from laboratory experiments do not expect consistent prediction accuracy due to complicated relation between materials and mix proportions. In this study, deep neural network model(Deep-LTS), which was based on experimental test results performed on various mixing conditions, was applied to predict the unconfined compressive strength. It was found that the unconfined compressive strength LTS at a given mixing ratio could be resonable estimated using proposed Deep-LTS.