• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.641-653
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• 2023

As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.4
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• pp.112-118
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• 2013

The selection of appropriate ground motions and reasonable modification are becoming increasingly critical in reliable prediction on seismic performance of structures. A widely used amplitude scaling approach is not sufficient for robust structural evaluation considering a site specific seismic hazard because only one spectral value is matched to the design spectrum typically at the structural fundamental period. Hence alternative approaches for ground motion selection and modifications have been suggested. However, there is no means to evaluate such methodologies yet. In this study, it is focused to describe the main questions resided in the amplitude scaling approach and to propose a regression model for structural damage as point of comparison. Spectrum compatible approach whose resulting spectrum matches the design spectrum at the entire range of the structural period is considered as alternative to be compared to the amplitude scaling approach. The design spectrum is generated according to ASCE7-05.

• Journal of the Korean Institute of Gas
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• v.21 no.3
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• pp.39-45
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• 2017

The LNG storage tank as core facility of LNG industry is mainly composed of the inner tank of nikel 9% steel and the outer tank of prestressed concrete. To respond proactively increased risk of structure performance deterioration due to fatigue of the inner tank and durability reduction of the outer tank, life evaluation program for LNG storage tank is needed. In this study, life evaluation program for LNG storage tank was developed to assess fatigue of the inner tank and durability(carbonation and chloride attack) of the outer tank. By defining the main three scenarios in the inner tank, the fatigue life analysis is conducted from structural analysis and Miner's damage rule. Carbonation progress of the outer tank is predicted according to thickness of cover concrete by using carbon dioxide contents and data of penetration depth. To consider a variety of input conditions and a reliability in results of chloride attack, the evaluation of choride attack for the outer tank is constructed through Life-365 program of open source.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.263-286
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• 2022

This study acquires NBA statistical information for a total of 32 years from 1990 to 2022 using web crawling, observes variables of interest through exploratory data analysis, and generates related derived variables. Unused variables were removed through a purification process on the input data, and correlation analysis, t-test, and ANOVA were performed on the remaining variables. For the variable of interest, the difference in the mean between the groups that advanced to the playoffs and did not advance to the playoffs was tested, and then to compensate for this, the average difference between the three groups (higher/middle/lower) based on ranking was reconfirmed. Of the input data, only this year's season data was used as a test set, and 5-fold cross-validation was performed by dividing the training set and the validation set for model training. The overfitting problem was solved by comparing the cross-validation result and the final analysis result using the test set to confirm that there was no difference in the performance matrix. Because the quality level of the raw data is high and the statistical assumptions are satisfied, most of the models showed good results despite the small data set. This study not only predicts NBA game results or classifies whether or not to advance to the playoffs using machine learning, but also examines whether the variables of interest are included in the major variables with high importance by understanding the importance of input attribute. Through the visualization of SHAP value, it was possible to overcome the limitation that could not be interpreted only with the result of feature importance, and to compensate for the lack of consistency in the importance calculation in the process of entering/removing variables. It was found that a number of variables related to three points and errors classified as subjects of interest in this study were included in the major variables affecting advancing to the playoffs in the NBA. Although this study is similar in that it includes topics such as match results, playoffs, and championship predictions, which have been dealt with in the existing sports data analysis field, and comparatively analyzed several machine learning models for analysis, there is a difference in that the interest features are set in advance and statistically verified, so that it is compared with the machine learning analysis result. Also, it was differentiated from existing studies by presenting explanatory visualization results using SHAP, one of the XAI models.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.846-851
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• 2005

In this paper, we propose Genetic Algorithms(GAs)-based Optimized Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional neural networks and can be generated in a dynamic manner. As each node of PNN structure, we use several types of high-order polynomial such as linear, quadratic and modified quadratic, and it is connected as various kinds of multi-variable inputs. The conventional PNN depends on the experience of a designer that select the number of input variables, input variable and polynomial type. Therefore it is very difficult to organize optimized network. The proposed algorithm leads to identify and select the number of input variables, input variable and polynomial type by using Genetic Algorithms(GAs). The aggregate performance index with weighting factor is proposed as well. The study is illustrated with tile NOx omission process data of gas turbine power plant for application to nonlinear process. In the sequel the proposed model shows not only superb predictability but also high accuracy in comparison to the existing intelligent models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.593-601
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• 2021

The axial fan is an element of a blower used for ventilation in various industrial fields. Many studies on aerodynamic performance have been conducted to assess axial fans using fluid dynamics. The subject was a large axial fan size, 1800 mm in diameter with 100 horsepower. The blower's axial fan consisted of blades, hubs, hub caps, and bosses are important components. The blade design has a great influence on the aerodynamic performance. 3D point data is extracted using an aerodynamic performance prediction program, and a 3D modeling shape is generated. The blades and hubs, which are important components, can be easily modified if processed by cutting owing to the environment in which blades and hubs are manufactured through die casting or gravity casting. In this study, the structural safety of components and the analysis results of weak areas at the rated operating speed of the axial fan were verified using the maximum stress and safety factor. The tip clearance reflected in the design was the rotation of the blade. To check whether there is interference with other components, the displacement result was derived to verify the structural safety of the axial fan.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.12
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• pp.1166-1173
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• 2011

Electro-Mechanical Ignition Safety Device(EMISD) for solid rocket motor is designed and manufactured. The EMISD utilizes a true rotary solenoid for arming mechanism and an electric squib(initiator) for generating ignition energy. In order to prove the ignition capability of the EMISD, 10-cc Closed Bomb Test(CBT) is performed, which measures the pressure built by high temperature and high pressure gas generated by operating EMISD. The pressure built in the free volume of 10-cc closed bomb and the opening time of the ignition gas outlet are calculated using one dimensional gas dynamic model which is composed of the ideal gas equation and mass-energy conservation equation. Comparing the test result with model prediction, it is realized that the pressure built in the free volume of closed bomb due to the firing of EMISD, has the efficiency ratio of about 34%.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.80-90
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• 2001

The aim of this study is to develop the methodology which enables to identify the mechanical properties of element such as stress intensity factor by using the AE parameters. Considering the multivariate and nonlinear properties of AE parameters such as ringdown count, rise time, energy, event duration and peak amplitude from fatigue cracks of machine element the principal component regression(PCR) and artificial neural network(ANN) models for the estimation of stress intensity factor were developed and validated. The AE parameters were found to be very significant to estimate the stress intensity factor. Since the statistical values including correlation coefficients, standard mr of calibration, standard error of prediction and bias were stable, the PCR and ANN models for stress intensity factor were very robust. The performance of ANN model for unknown data of stress intensity factor was better than that of PCR model.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.1059-1065
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• 2008

Rainfall data is the most basic input data to analyze the hydrological phenomena and can be missing due to various reasons. In this research, a neural network based model to estimate missing rainfall data as approximate values was developed for 12 rainfall stations in the Soyang river basin to improve existing methods. This approach using neural network has shown to be useful in many applications to deal with complicated natural phenomena and displayed better results compared to the popular offline estimating methods, such as RDS(Reciprocal Distance Squared) method and AMM(Arithmetic Mean Method). Additionally, we proposed automated data reconciliation systems composed of a neural network learning processer to be capable of real-time reconciliation to transmit reliable hydrological data online.

• Journal of the Earthquake Engineering Society of Korea
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• v.7 no.3
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• pp.69-77
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• 2003

This paper presents test results on eight reduced beam section(RBS) steel moment connections. The testing program addressed bolted versus welded web connection and panel zone(PZ) strength as key variables, Specimens with medium PZ strength were designed to promote energy dissipation from both PZ and RBS regions such that the requirement for expensive doublet plates could be reduced. Both strong and medium PZ specimens with a welded web connection were able to provide satisfactory connection rotation capacity for special moment-resisting frames. On the other hand, specimens with a bolted web connection performed poorly due to premature brittle fracture of the beam flange of the weld access hole. If fracture within the beam flange groove weld was avoided using quality welding, the fracture tended to move into the beam flange base metal of the weld access hole. Plausible explanation of a higher incidence of base metal fracture in bolted web specimens was presented. The measured strain data confirmed that the classical beam theory dose not provide reliable shear transfer prediction in the connection. The practice of providing web bolts uniformly along the beam depth was brought into question. Criteria for a balanced PZ strength improves the plastic rotation capacity while reduces the amount of beam distortion ore also proposed.