• Title/Summary/Keyword: Artificial Neural Networks

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Application of Artificial Neural Networks to Predict Ultimate Shear Capacity of PC Vertical Joints (PC 수직 접합부의 극한 전단 내력 예측에 대한 인공 신경 회로망의 적용)

  • 김택완;이승창;이병해
    • Computational Structural Engineering
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    • v.9 no.2
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    • pp.93-101
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    • 1996
  • An artificial neural network is a computational model that mimics the biological system of the brain and it consists of a number of interconnected processing units where it can reasonably infer by them. Because the neural network is particularly useful for evaluating systems with a multitude of nonlinear variables, it can be used in experimental results predictions, in structural planning and in optimum design of structures. This paper describes the basic theory related to the neural networks and discusses the applicability of neural networks to predict the ultimate shear capacity of the precast concrete vertical joints by comparing the neural networks with a conventional method such as regression.

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A Study on Subsidence of Soft Ground Using Artificial Neural Network (인공신경망을 이용한 DCM 처리된 연약지반 침하에 대한 연구)

  • Kang, Yoon-Kyung;Jang, Won-Yil
    • Journal of Advanced Marine Engineering and Technology
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    • v.34 no.6
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    • pp.914-921
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    • 2010
  • When industrial structures are constructed on soft ground, ground subsidence is occurred by problems of bearing capacity. To protect ground subsidence have to improve soft ground, and have to predict settlement estimation for reasonable construction. Artificial Neural Networks(ANN) is adopted for prediction of settlement of construction during the initial design. In the study, Artificial Neural Networks are applied to predict the settlement estimation of initial condition ground and ground improved by D.C.M method. Also, this study compares results of Artificial Neural Networks and results of continuum analysis using Mohr-Coulomb models. In result, settlements of initial condition ground decreased over 0.7 times. Also, by comparing ANN and continuum analysis, coefficient of determination was comparatively high value 0.79. Thought this study, it was confirmed that settlements of improvement ground is predicted using laboratory experiment data.

Forecasting LNG Freight rate with Artificial Neural Networks

  • Lim, Sangseop;Ahn, Young-Joong
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.7
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    • pp.187-194
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    • 2022
  • LNG is known as the transitional energy source for the future eco-friendly, attracting enormous market attention due to global eco-friendly regulations, Covid-19 Pandemic, Russia-Ukraine War. In addition, since new LNG suppliers such as the U.S. and Australia are also diversifying, the LNG spot market is expected to grow. On the other hand, research on the LNG transportation market has been marginalized. Therefore, this study attempted to predict short-term LNG 160K spot rates and compared the prediction performance between artificial neural networks and the ARIMA model. As a result of this paper, while it was difficult to determine the superiority and superiority of ARIMA and artificial neural networks, considering the relative free of ANN's contraints, we confirmed the feasibility of ANN in LNG 160K spot rate prediction. This study has academic significance as the first attempt to apply an artificial neural network to forecasting LNG 160K spot rates and are expected to contribute significantly in practice in that they can improve the quality of short-term investment decisions by market participants by increasing the accuracy of short-term prediction.

Application of Neural Networks in Aluminum Corrosion

  • Powers, John;Ali, M. Masoom
    • Journal of the Korean Data and Information Science Society
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    • v.11 no.2
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    • pp.157-172
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    • 2000
  • Metal containers represent a situation where a specific metal is exposed to a wide variety of electrolytes of varying degrees of corrosivity. For example, hundreds, if not thousands of different products are packaged in an aluminum beverage can. These products vary in pH, chloride concentration and other natural or artificial ingredients which can effect the type and severity of potential corrosion. Both localized (perforation) and uniform corrosion (metal dissolution without the onset of pitting) may occur in the can. A quick test or series of tests which could predict the propensity towards both types of corrosion would be useful to the manufacturer. Electrochemical noise data is used to detect the onset and continuation of pitting corrosion. Specific noise parameters such as the noise resistance (the potential noise divided by the current noise) have been used to both detect pitting corrosion and also to estimate the pitting severity. The utility of noise resistance and other electrochemical parameters has been explored through the application of artificial neural networks. The versatility of artificial neural networks is further demonstrated by combing electrochemical data with electrolyte properties such as pH and chloride concentration to predict both the severity of both localized and uniform corrosion.

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Comparison of the BOD Forecasting Ability of the ARIMA model and the Artificial Neural Network Model (ARIMA 모형과 인공신경망모형의 BOD예측력 비교)

  • 정효준;이홍근
    • Journal of Environmental Health Sciences
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    • v.28 no.3
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    • pp.19-25
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    • 2002
  • In this paper, the water quality forecast was performed on the BOD of the Chungju Dam using the ARIMA model, which is a nonlinear statistics model, and the artificial neural network model. The monthly data of water quality were collected from 1991 to 2000. The most appropriate ARIMA model for Chungju dam was found to be the multiplicative seasonal ARIMA(1,0,1)(1,0,1)$_{12}$, model. While the artificial neural network model, which is used relatively often in recent days, forecasts new data by the strength of a learned matrix like human neurons. The BOD values were forecasted using the back-propagation algorithm of multi-layer perceptrons in this paper. Artificial neural network model was com- posed of two hidden layers and the node number of each hidden layer was designed fifteen. It was demonstrated that the ARIMA model was more appropriate in terms of changes around the overall average, but the artificial neural net-work model was more appropriate in terms of reflecting the minimum and the maximum values.s.

THE APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO LANDSLIDE SUSCEPTIBILITY MAPPING AT JANGHUNG, KOREA

  • LEE SARO;LEE MOUNG-JIN;WON JOONG-SUN
    • Proceedings of the KSRS Conference
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    • 2004.10a
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    • pp.294-297
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    • 2004
  • The purpose of this study was to develop landslide susceptibility analysis techniques using artificial neural networks and then to apply these to the selected study area of Janghung in Korea. We aimed to verify the effect of data selection on training sites. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use was constructed. Thirteen landslide-related factors were extracted from the spatial database. Using these factors, landslide susceptibility was analyzed using an artificial neural network. The weights of each factor were determined by the back-propagation training method. Five different training datasets were applied to analyze and verify the effect of training. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. The results of the landslide susceptibility maps were verified and compared using landslide location data. GIS data were used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool to analyze landslide susceptibility.

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Application of Artificial Neural Networks to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

  • Oh, Sang Hoon;Kim, Kyungmin;Harry, Ian W.;Hodge, Kari A.;Kim, Young-Min;Lee, Chang-Hwan;Lee, Hyun Kyu;Oh, John J.;Son, Edwin J.
    • The Bulletin of The Korean Astronomical Society
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    • v.39 no.2
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    • pp.107.1-107.1
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    • 2014
  • We apply a machine learning algorithm, artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. We also evaluate the gravitational-wave data within a few seconds of the selected short gamma-ray bursts' event times using the trained networks and obtain the false alarm probability. We suggest that artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.

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A study on sequential iterative learning for overcoming catastrophic forgetting phenomenon of artificial neural network (인공 신경망의 Catastrophic forgetting 현상 극복을 위한 순차적 반복 학습에 대한 연구)

  • Choi, Dong-bin;Park, Young-beom
    • Journal of Platform Technology
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    • v.6 no.4
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    • pp.34-40
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    • 2018
  • Currently, artificial neural networks perform well for a single task, but NN have the problem of forgetting previous learning by learning other kinds of tasks. This is called catastrophic forgetting. To use of artificial neural networks in general purpose this should be solved. There are many efforts to overcome catastrophic forgetting. However, even though there was a lot of effort, it did not completely overcome the catastrophic forgetting. In this paper, we propose sequential iterative learning using core concepts used in elastic weight consolidation (EWC). The experiment was performed to reproduce catastrophic forgetting phenomenon using EMNIST data set which extended MNIST, which is widely used for artificial neural network learning, and overcome it through sequential iterative learning.

A Personal Credit Rating Using Convolutional Neural Networks with Transformation of Credit Data to Imaged Data and eXplainable Artificial Intelligence(XAI) (신용 데이터의 이미지 변환을 활용한 합성곱 신경망과 설명 가능한 인공지능(XAI)을 이용한 개인신용평가)

  • Won, Jong Gwan;Hong, Tae Ho;Bae, Kyoung Il
    • The Journal of Information Systems
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    • v.30 no.4
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    • pp.203-226
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    • 2021
  • Purpose The purpose of this study is to enhance the accuracy score of personal credit scoring using the convolutional neural networks and secure the transparency of the deep learning model using eXplainalbe Artifical Inteligence(XAI) technique. Design/methodology/approach This study built a classification model by using the convolutional neural networks(CNN) and applied a methodology that is transformation of numerical data to imaged data to apply CNN on personal credit data. Then layer-wise relevance propagation(LRP) was applied to model we constructed to find what variables are more influenced to the output value. Findings According to the empirical analysis result, this study confirmed that accuracy score by model using CNN is highest among other models using logistic regression, neural networks, and support vector machines. In addition, With the LRP that is one of the technique of XAI, variables that have a great influence on calculating the output value for each observation could be found.

Artificial Neural Networks for Flood Forecasting Using Partial Mutual Information-Based Input Selection

  • Jae Gyeong Lee;Li Li;Kyung Soo Jun
    • Proceedings of the Korea Water Resources Association Conference
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    • 2023.05a
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    • pp.363-363
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    • 2023
  • Artificial Neural Networks (ANN) is a powerful tool for addressing various practical problems and it has been extensively applied in areas of water resources. In this study, Artificial Neural Networks (ANNs) were developed for flood forecasting at specific locations on the Han River. The Partial Mutual Information (PMI) technique was used to select input variables for ANNs that are neither over-specified nor under-specified while adequately describing the underlying input-output relationships. Historical observations including discharges at the Paldang Dam, flows from tributaries, water levels at the Paldang Bridge, Banpo Bridge, Hangang Bridge, and Junryu gauge station, and time derivatives of the observed water levels were considered as input candidates. Lagged variables from current time t to the previous five hours were assumed to be sufficient in this study. A three-layer neural network with one hidden layer was used and the neural network was optimized by selecting the optimal number of hidden neurons given the selected inputs. Given an ANN architecture, the weights and biases of the network were determined in the model training. The use of PMI-based input variable selection and optimized ANNs for different sites were proven to successfully predict water levels during flood periods.

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