• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.843-856
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• 2019

Real-time flood prediction has an important role in significantly reducing potential damage caused by floods for urban residential areas located downstream of river basins. This paper presents an effective approach for flood forecasting based on the construction of a deep neural network (DNN) model. In addition, this research depends closely on the open-source software library, TensorFlow, which was developed by Google for machine and deep learning applications and research. The proposed model was applied to forecast the flowrate one, two, and three days in advance at the Son Tay hydrological station on the Red River, Vietnam. The input data of the model was a series of discharge data observed at five gauge stations on the Red River system, without requiring rainfall data, water levels and topographic characteristics. The research results indicate that the DNN model achieved a high performance for flood forecasting even though only a modest amount of data is required. When forecasting one and two days in advance, the Nash-Sutcliffe Efficiency (NSE) reached 0.993 and 0.938, respectively. The findings of this study suggest that the DNN model can be used to construct a real-time flood warning system on the Red River and for other river basins in Vietnam.

• Geophysics and Geophysical Exploration
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• v.2 no.2
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• pp.112-121
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• 1999

This article reviews the development of geophysical inverse theory. In a series of articles published in 1967, 1968, and 1979, G. Backus and F. Gilbert a trade-off between model resolution and estimation errors in geophysical inverse problems, and gave a criterion to compromise the reciprocal relation. Although the criterion was not clear in the physical point of view, it had been extensively used in the interpretation of geophysical date in the 1970s. This was the starting point of the fruitful development of inverse theory in geophysics. A reasonable criterion to compromise the reciprocal relation was derived to solve linear problems by D. D. jackson in 1979, introducing the concept of a priori information about unknown model parameters. This Jackson's approach was extended to solve nonlinear problems on the basis o probabilistic approach to the inverse problems formulated by A. Tarantola and B. Vallete in 1982. At the end of 1980s ABIC (Akaike Bayesian Information Criterion) was introduced for selecting a more reasonable model in geophysics. Now the date inversion is regarded as the process of extracting new information from observed data, combining in with a priori information about model parameters, and constructing a more clear image of model.

• Journal of Korean Association for Spatial Structures
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• v.14 no.3
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• pp.93-100
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• 2014

Ethylene Tetrafluoroethylene (ETFE) has been widely used in long-span buildings because of its light weight and high transparency. This paper studies the short and long term creep behaviour of ETFE foil. A series of short-term creep and recovery tests were performed, in which the residual strain was observed. A long-term creep test of the ETFE foil was also performed over 110 days. A viscoelastic-plastic model was then established to describe the short-term creep and recovery behaviour. The model contains a traditional multi-Kelvin part and an added steady-flow component to represent the viscoelastic and viscoplastic behaviour, respectively. The model successfully fit the data for three stresses and six temperatures. Additionally, time-temperature equivalency was adopted to predict the long-term creep behaviour of ETFE foil. Horizontal shifting factors were determined from the process of shifting creep-curves at six temperatures. The long-term creep behaviours at three temperatures were predicted. Finally, the long-term creep test showed that the short-term creep test at identical temperatures insufficiently predicted additional creep behaviour, and the long-term test verified the horizontal shifting factors derived from the time-temperature equivalency.

• Computers and Concrete
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• v.1 no.3
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• pp.261-284
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• 2004

Two enhancements to a recently developed plastic-damage-contact model for concrete are presented. The model itself, which uses planes of degradation that can undergo damage and separation but that can regain contact according to a contact law, is described. The first enhancement is a new damage evolution function which provides a completely smooth transition from the undamaged to the damaged state and from pre-peak to post-peak regions. The second is an improved contact function that governs the potential degree of contact with increasing opening on a crack plane. The use of a damage evolution function with a pre-peak has implications for the consistent tangent matrix/stress recovery algorithm developed for the model implementation, and amendments to this algorithm to accommodate the new function are described. A series of unpublished experimental tests on notched specimens undertaken in Cardiff in the mid 1990s are then described. These include notched beam tests as well as prismatic and cylindrical torsion tests. The tests are then considered in three dimensional finite element analyses using the modified Craft model implemented in the finite element program LUSAS. Comparisons between experimental and numerical data show reasonable agreement except that the numerical simulations do not fully describe the latter stages of the softening responses for the torsion examples. Finally, it is concluded that the torsion tests described provide useful benchmark examples for the validation of three-dimensional numerical models for concrete.

• Structural Engineering and Mechanics
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• v.16 no.2
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• pp.127-139
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• 2003

Reinforced concrete buildings with shearwalls are very efficient to resist earthquake disturbances. In general, reinforced concrete frames are governed by flexure and shearwalls are governed by shear. If a structure included both frames and shearwalls, it is generally governed by shearwalls. However, the ductility of ordinary reinforced concrete is very limited. To improve the ductility, a series of tests on framed shearwalls made of corrugated steel was performed previously and the experimental results were compared with ordinary reinforced concrete frames and shearwalls. It was found that ductility of framed shearwalls could be greatly improved if the thickness of the corrugated steel wall is appropriate to the surrounding reinforced concrete frame. In this paper, an analytical model is developed to predict the horizontal load-displacement relationship of hybrid reinforced concrete frame-steel wall systems according to the analogy of truss models. This analytical model is based on equilibrium and compatibility conditions as well as constitutive laws of corrugated steel. The analytical predictions are compared with the results of tests reported in the previous paper. It is found that proposed analytical model can predict the test results with acceptable accuracy.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.2
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• pp.265-274
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• 1992

A mathematical model was proposed to predict the fate of a priority organic pollutant, anthracene, accidently spilled into a stream. The model consists of 6 differential equations with 5 input variables and 9 rate constants. Volatilization, biodegradation, adsorption/desorption, photodegradation as well as the convective inputs and outputs are included in the model. As a result of a series of dynamic simulations and sensitivity analyses under the given conditions, the concentrations of the organic chemical could be predicted within a detection limit in the stream. It was also suggested that the rate constant for diffusion/transport and adsorption rate constant are the most influential ones for predicting the chemical conentrations in dissolved and particulate phase. The model proposed appears to be a useful tool for assessing chemical spills.

• Structural Engineering and Mechanics
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• v.65 no.3
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• pp.263-274
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• 2018

In this study, we propose a frequency domain spectral element method (SEM) for the vibration analysis of a multi-span beam subjected to a moving point force. This study is an extension of the authors' previous study for a single-span beam subjected to a moving point force, where the two-element model-based SEM was applied. In this study, each span of a multi-span beam is represented by the Timoshenko beam model and the moving point force is transformed into the frequency domain as a series of each stationary point force distributed on the multi-span beam. The span at which a stationary point force is located is represented by two-element model, but all other spans are represented by one-element models. The vibration responses to a moving point force are obtained by superposing all individual vibration responses generated by each stationary point force. The high accuracy and computational efficiency of the proposed SEM are verified by comparing the solutions by SEM with exact analytical solutions by the integral transform method (ITM) as well as the solutions by the finite element method (FEM).

• The Journal of Asian Finance, Economics and Business
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• v.7 no.4
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• pp.59-70
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• 2020

The purpose of this study is to construct the ACD model for the block trading volume duration. The ACD model based on the block trading volume duration is referred to as Volume ACD (VACD) in this study. By integrating with GARCH-type models, the VACD based GARCH type models, which include VACD-GARCH, VACD-IGARCH and VACD-FIGARCH models, are set up. This study selects Chunghwa Telecom (CHT) Inc., offering the America Depository Receipt (ADR) in NYSE, to investigate the block trading volume duration in Taiwanese equity market. The empirical results indicate that the long memory in volume duration series increases dependence at level of volatility clustering by VACD (2,1)-FIGARCH (3,d,1) model. Moreover, the VACD (2,1)-IGARCH (1,1) exhibits relatively better performance of prediction on capturing block trading volume duration. This volatility model is more appropriate in this study to portray the change of the CHT Inc. prices and provides more information about the volatility process for investment strategy, which can be a reference indicator of financial asset pricing, hedging strategy and risk management.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.5
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• pp.604-612
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• 2008

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. This study focuses on applying the Autoregressive Error (ARE) model for analyzing the ozone data at middle part of the Gyeonggi-Do, Anyang monitoring site in Korea. In the ARE model, eight meteorological variables and four pollution variables are used as the explanatory variables. The eight meteorological variables are daily maximum temperature, wind speed, amount of cloud, global radiation, relative humidity, rainfall, dew point temperature, and water vapor pressure. The four air pollution variables are sulfur dioxide $(SO_2)$, nitrogen dioxide $(NO_2)$, carbon monoxide (CO), and particulate matter 10 (PM10). The result shows that ARE models both overall and monthly data are suited for describing the oBone concentration. In the ARE model for overall ozone data, ozone concentration can be explained about 71% to by the PM10, global radiation and wind speed. Also the four types of ARE models for high level of ozone data (over 80 ppb) have been analyzed. In the best ARE model for high level of ozone data, ozone can be explained about 96% by the PM10, daliy maximum temperature, and cloud amount.