• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.142-142
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• 2021

An arbitrary representation of an urban drainage sewer system was devised using a geographic information system (GIS) tool in order to calculate the surface and subsurface flow interaction for simulating urban flood. The proposed methodology is a mean to supplement the unavailability of systematized drainage system using high-resolution digital elevation(DEM) data in under-developed countries. A modified DEM was also developed to represent the flood propagation through buildings and road system from digital surface models (DSM) and barely visible streams in digital terrain models (DTM). The manhole, sewer pipe and storm drain parameters are obtained through field validation and followed the guidelines from the Plumbing law of the Philippines. The flow discharge from surface to the devised sewer pipes through the storm drains are calculated. The resulting flood simulation using the modified DEM was validated using the observed flood inundation during a rainfall event. The proposed methodology for constructing a hypothetical drainage system allows parameter adjustments such as size, elevation, location, slope, etc. which permits the flood depth prediction for variable factors the Plumbing law. The research can therefore be employed to simulate urban flood forecasts that can be utilized from traffic advisories to early warning procedures during extreme rainfall events.

• Journal of Ocean Engineering and Technology
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• v.38 no.3
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• pp.103-114
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• 2024

Remote sensing wave observation data are crucial when analyzing ocean waves, the main external force of coastal disasters. Nevertheless, it has limitations in accuracy when used in low-wind environments. Therefore, this study collected the raw data from MIROS Wave and Current Radar (MWR) and wave radar at the Ieodo Ocean Research Station (IORS) and applied the optimal filter by combining filters provided by MIROS software. The data were validated by a comparison with South Jeju ocean buoy data. The results showed it maintained accuracy for significant wave height, but errors were observed in significant wave periods and extreme waves. Hence, this study used an artificial neural network (ANN) to improve these errors. The ANN was generalized by separating the data into training and test datasets through stratified sampling, and the optimal model structure was derived by adjusting the hyperparameters. The application of ANN effectively improved the accuracy in significant wave periods and high wave conditions. Consequently, this study reproduced past wave data by enhancing the reliability of the MWR, contributing to understanding wave generation and propagation in storm conditions, and improving the accuracy of wave prediction. On the other hand, errors persisted under high wave conditions because of wave shadow effects, necessitating more data collection and future research.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.745-745
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• 2002

Both geophysical and geospatial data provide important information in the establishment of the optimal telecommunication systems especially in the mobile telecommunication environment. The objective of this study is to utilize geophysical properties and geospatial information in the analysis of the telecommunication environment through point-to-point wave property modeling. Geophysical properties associated with wave propagation parameters of the earth surface were analyzed based on hierarchical land classification using Landsat ETM＋ and IKONOS images. Three-dimensional geospatial information was obtained by processing stereo aerial images. The results show that the accurate geospatial information and reliable geosphysical property of the surface improve the prediction of receiving power of the receivers located near corners of the buildings where diffractions occur. The wave property model developed from accurate telecommunication environment could be applied to optimal cell planning and delay time analysis.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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• pp.725-733
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• 2010

A simulator is proposed in this paper for predicting the RF signal level after propagating over sea and land surfaces. Various sea and land types and transmit/receive antenna patterns, as well as the locus of the transmit antenna, are considered for this simulator. At first, microwave reflection characteristics of various sea surfaces have been computed, based on an empirical formula which is developed in this study for the relation between the sea surface roughness and wind speed. Then, microwave reflections from land surfaces such as forests, agricultural areas, and bare surfaces, are computed using the first-order vector radiative transfer theory. Finally, the signal paths over sea and land surfaces are found using the ray tracing technique and the digital elevation model, and the signal level received by a receiving antenna is computed by the using the reflection coefficients of sea and land surfaces and the signal paths.

• Computers and Concrete
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• v.20 no.6
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• pp.627-634
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• 2017

Predicting the compressive strength of concrete is important to assess the load-carrying capacity of a structure. However, the use of blended cements to accrue the technical, economic and environmental benefits has increased the complexity of prediction models. Artificial Neural Networks (ANNs) have been used for predicting the compressive strength of ordinary Portland cement concrete, i.e., concrete produced without the addition of supplementary cementing materials. In this study, models to predict the compressive strength of blended cement concrete prepared with a natural pozzolan were developed using regression models and single- and 2-phase learning ANNs. Back-propagation (BP), Levenberg-Marquardt (LM) and Conjugate Gradient Descent (CGD) methods were used for training the ANNs. A 2-phase learning algorithm is proposed for the first time in this study for predictive modeling of the compressive strength of blended cement concrete. The output of these predictive models indicates that the use of a 2-phase learning algorithm will provide better results than the linear regression model or the traditional single-phase ANN models.

• Wind and Structures
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• v.38 no.1
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• Corrosion Science and Technology
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• v.4 no.4
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• pp.136-139
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• 2005

The effect of oxidation and hot corrosion on the solid particle erosion was investigated for hot-pressed silicon nitride using as-polished, pre-oxidized and pre-corroded specimens by molten sodium sulfates. Erosion tests were performed at 22, 500 and $900^{\circ}C$ using angular silicon carbide particles of mean diameter $100{\mu}m$. Experimental results show that solid particle erosion rate of silicon nitride increases with increasing temperature for as-polished or pre-oxidized specimens in consistent with the prediction of a theoretical model. Erosion rate of pre-oxidized specimens is lower than that of as-polished specimens at $22^{\circ}C$, but it is higher at $900^{\circ}C$. Lower erosion rate at $22^{\circ}C$ in the pre-oxidized specimens is attributed due to the blunting of surface flaws, and the higher erosion rate at $900^{\circ}C$ is due to brittle lateral cracking. Erosion rate of pre-corroded specimens decreases with increasing temperature. Less erosion at $900^{\circ}C$ than at $22^{\circ}C$ is associated with the liquid corrosion products sealing off pores at $900^{\circ}C$ and the absence of inter-granular crack propagation observed at $22^{\circ}C$.

• The Journal of Engineering Geology
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• v.5 no.2
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• pp.201-213
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• 1995

Understanding of the fracture processes in rock mass for hydrogeology necessitates such information as fracture mechanics including genesis, propagation, termination, and the relation of fracture distribution to geologic structures and fracture modelling, etc. A current status of information on fracture for groundwater flow in rock mass, however, is very paucity except on a few special fields throughout the world. The desired and reasonable approach method in the evaluation on the groundwater flow in fractured rock mass must be based on the thorough understanding of fracture processes and a simplified model representing fracture properties which would be met to natural conditions for the interpretation and prediction.

• The Transactions of the Korea Information Processing Society
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• v.7 no.10
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• pp.3195-3200
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• 2000

this paper is a stucy onthe preductionof multi-media traffic flow for the realizationof optimum ATM congestion control. In ATM network it is expected that the characteristic of multi-media traffic flow is varied slowly with a time. Fjor the simulation, time-variable multi-media traffic is penerated using possion distribution(connect calls per process time).\, gamma distribution(transmission rate per a call) and exponential distribution(holding time per a call). And using back-propagation neural netwok and proposed tripple neural network, the simulation to predict generaed traffic is executed. From the result,it's capability is shown that the proposed neural network model can be used in the predictionof ATM traffic flow.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.