• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.253-256
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• 1996

Rainfall is one of the major and complicated elements of hydrologic system. Accurate prediction of rainfall is very important to mitigate storm damage. The neural network is a good model to be applied for the classification problem, large combinatorial optimization and nonlinear mapping. In this dissertation, rainfall predictions by the neural network theory were presented. A multi-layer neural network was constructed. The network learned continuous-valued input and output data. The network was used to predict rainfall. The online, multivariate, short term rainfall prediction is possible by means of the developed model. A multidimensional rainfall generation model is applied to Seoul metropolitan area in order to generate the 10-minute rainfall. Application of neural network to the generated rainfall shows good prediction. Also application of neural network to 1-hour real data in Seoul metropolitan area shows slightly good predictions.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.252-254
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• 2003

In this study, it was purposed to develop the new method for the prediction of pollutant concentration in road tunnels. The new method was the use of artificial neural network with the back-propagation algorithm which can model the non-linear system of tunnel environment. This network system was separated into two parts as the visibility and the CO concentration. For this study, data was collected from two highway road tunnels on Yeongdong Expressway. The tunnels have two lanes with one-way direction and adopt the longitudinal ventilation system. The actually measured data from the tunnels was used to develop the neural network system for the prediction of pollutant concentration. The output results from the newly developed neural network system were analysed and compared with the calculated values by PIARC method. Results showed that the prediction accuracy by the neural network system was approximately five times better than the one by PIARC method. ill addition, the system predicted much more accurately at the situation where the drivers have to be stayed for a while in tunnels caused by the low velocity of vehicles.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.7-12
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• 2003

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock or other economic markets. Most previous experiments used the neural network models for the stock market forecasting. The KOSPI 200 (Korea Composite Stock Price Index 200) is modeled by using different neural networks and fuzzy logic. In this paper, the neural network, the dynamic polynomial neural network (DPNN) and the fuzzy logic employed for the prediction of the KOSPI 200. The prediction results are compared by the root mean squared error (RMSE) and scatter plot, respectively. The results show that the performance of the fuzzy system is little bit worse than that of the DPNN but better than that of the neural network. We can develop the desired fuzzy system by optimization methods.

• Journal of the Korean Society of Visualization
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• v.20 no.2
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• pp.78-85
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• 2022

In these days, the rapid development in prediction technology using artificial intelligent is being applied in a variety of engineering fields. Especially, dimensionality reduction technologies such as autoencoder and convolutional neural network have enabled the classification and regression of high-dimensional data. In particular, pixel level prediction technology enables semantic segmentation (fine-grained classification), or physical value prediction for each pixel such as depth or surface normal estimation. In this study, the pressure distribution of the ship's surface was estimated at the pixel level based on the artificial neural network. First, a potential flow analysis was performed on the hull form data generated by transforming the baseline hull form data to construct 429 datasets for learning. Thereafter, a neural network with a U-shape structure was configured to learn the pressure value at the node position of the pretreated hull form. As a result, for the hull form included in training set, it was confirmed that the neural network can make a good prediction for pressure distribution. But in case of container ship, which is not included and have different characteristics, the network couldn't give a reasonable result.

• Agricultural and Biosystems Engineering
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• v.1 no.2
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• pp.67-72
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• 2000

A dynamic draft model is necessary to analyze mechanics of tillage and to design optimal tillage tools. In order to deal with draft dynamics, a neural network paradigm was applied to develop dynamic draft models. For the development of the models, three kinds of tillage tools were used to measure drafts in the soil bin and a time lagged recurrent neural network was developed. The neural network had a structure to predict dynamic draft, having a function of one-step-ahead prediction. A procedure for network prediction model identification was established. The results show promising modeling of the dynamic drafts with developed neural network.

• Journal of Industrial Technology
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• v.41 no.1
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• pp.1-6
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• 2021

Fly ash and silica fume belong to industry by-products that can be used to produce concrete. This study shows the model of a neural network to evaluate the strength development of blended concrete containing fly ash and silica fume. The neural network model has four input parameters, such as fly ash replacement content, silica fume replacement content, water/binder ratio, and ages. Strength is the output variable of neural network. Based on the backpropagation algorithm, the values of elements in the hidden layer of neural network are determined. The number of neurons in the hidden layer is confirmed based on trial calculations. We find (1) neural network can give a reasonable evaluation of the strength development of composite concrete. Neural network can reflect the improvement of strength due to silica fume additions and can consider the reductions of strength as water/binder increases. (2) When the number of neurons in the hidden layer is five, the prediction results show more accuracy than four neurons in the hidden layer. Moreover, five neurons in the hidden layer can reproduce the strength crossover between fly ash concrete and plain concrete. Summarily, the neural network-based model is valuable for design sustainable composite concrete containing silica fume and fly ash.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.203-206
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• 1996

Prediction of firm bankruptcy have been extensively studied in accounting, as all stockholders in a firm have a vested interest in monitoring its financial performance. The objective of this paper is to develop the hybrid models for bankruptcy prediction. The proposed hybrid models are two phase. Phase one are (a) DA-assisted neural network, (b) Logit-assisted neural network, and (c) Genetic-assisted neural network. And, phase two are (a) DA-assisted Case based reasoning, and (b) Genetic-assisted Case based reasoning. In the variables selection, We are focusing on three alternative methods - linear discriminant analysis, logit analysis and genetic algorithms - that can be used empirically select predictors for hybrid model in bankruptcy prediction. Empirical results using Korean medium-sized firms data show that hybrid models are very promising neural network models and case based reasoning for bankruptcy prediction in terms of predictive accuracy and adaptability.

• Agricultural and Biosystems Engineering
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• v.3 no.1
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• pp.18-28
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• 2002

Neural network based models were developed and evaluated for predicting corn yield from aerial images based on 1998 and 1994 image data. The model used images in multi-spectral bands such as R, G, B, and IR (Red, Green, Blue and Infrared). The inputs to the neural network consisted of mean and standard deviation of multispectral bands of the aerial images. Performances of several neural network architectures using back-propagation with momentum were compared. The maximum yield prediction accuracy obtained was 97.81%. The BPNN model prediction accuracy could be enhanced by using more number of observations to the model, other data transformation techniques, or by performing optical calibration of the aerial image.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.769-782
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• 2020

Interest in fruit and vegetables has increased due to changes in consumer consumption patterns, socioeconomic status, and family structure. This study determined the factors influencing the demand for fruit and vegetables (strawberries, paprika, tomatoes and cherry tomatoes) using a panel of Rural Development Administration household-level purchases from 2010 to 2018 and compared the ability to the prediction performance. An artificial neural network model was constructed, linking household characteristics with final food expenditure. Comparing the analysis results of the artificial neural network with the results of the panel model showed that the artificial neural network accurately predicted the pattern of the consumer panel data rather than the fixed effect model. In addition, the prediction for strawberries was found to be heavily affected by the number of families, retail places and income, while the prediction for paprika was largely affected by income, age and retail conditions. In the case of the prediction for tomatoes, they were greatly affected by age, income and place of purchase, and the prediction for cherry tomatoes was found to be affected by age, number of families and retail conditions. Therefore, a more accurate analysis of the consumer consumption pattern was possible through the artificial neural network model, which could be used as basic data for decision making.

• Smart Structures and Systems
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• v.32 no.2
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• pp.83-99
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• 2023

The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.