• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.576-584
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• 2017

Traffic demand prediction result is a primary factor for decision making such as the traffic planning and operation. The existing traffic demand prediction 4-step model only covers the trip between the origin and the destination, and not the demand followed by the accessibility improvement, due to the characteristic of this model. Therefore, the purpose of this research is to improve the limitations of the existing model by developing the inter-city trip generation and trip distribution model with more accessibility. After calculating of the trip generation and trip distribution model with more accessibility, the sign of the accessibility coefficient was positive. Commuting was the most insensitive indicator, affected by external factors among the other trip purposes. The leisure trip was the most sensitive, affected by the trip fee. According to the result of comparison with each of estimated model and observational data, it was certain that the reliability and assumption of the model have been improved by discovering the reduced weighted average error rate, Root Mean Square Error (RMSE) and total error through the model with more accessibility compared with the existing one.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.1
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• pp.79-87
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• 2008

Seismic performance evaluation of structure requires an estimation of the structural performance in terms of displacement demand imposed by earthquakes on the structure. Incremental Dynamic Analysis(IDA) is a analysis method that has recently emerged to estimate structural performance under earthquakes. This method can obtained the entire range of structural performance from the linear elastic stage to yielding and finally collapse by subjecting the structure to increasing levels of ground acceleration. Most structures are expected to deform beyond the limit of linearly elastic behavior when subjected to strong ground motion. The nonlinear response history analysis(NRHA) among various nonlinear analysis methods is the most accurate to compute seismic performance of structures, but it is time-consuming and necessitate more efforts. The nonlinear approximate methods, which is more practical and reliable tools for predicting seismic behavior of structures, are extensively studied. The uncoupled modal response history analysis(UMRHA) is a method which can find the nonlinear reponse of the structures for ESDF from the pushover curve using NRHA or response spectrum. The direct spectrum analysis(DSA) is approximate nonlinear method to evaluate nonlinear response of structures, without iterative computations, given by the structural linear vibration period and yield strength from the pushover analysis. In this study, the practicality and the reliability of seismic performance of approximate nonlinear methods for incremental dynamic analysis of mixed building structures are to be compared.

• Journal of the Korean earth science society
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• v.39 no.6
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• pp.533-544
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• 2018

Rainfall is one of the most important meteorological variables in meteorology, agriculture, hydrology, natural disaster, construction, and architecture. Recently, satellite remote sensing is essential to the accurate detection, estimation, and prediction of rainfall. In this study, the accuracy of Integrated Multi-satellite Retrievals for GPM (IMERG) product, a composite rainfall information based on Global Precipitation Measurement (GPM) satellite was evaluated with ground observation data in the inland of Korea. The Automatic Weather Station (AWS)-based rainfall measurement data were used for validation. The IMERG and AWS rainfall data were collocated and compared during one year from January 1, 2016 to December 31, 2016. The coastal regions and islands were also evaluated irrespective of the well-known uncertainty of satellite-based rainfall data. Consequently, the IMERG data showed a high correlation (0.95) and low error statistics of Bias (15.08 mm/mon) and RMSE (30.32 mm/mon) in comparison to AWS observations. In coastal regions and islands, the IMERG data have a high correlation more than 0.7 as well as inland regions, and the reliability of IMERG data was verified as rainfall data.

• Journal of the Korean GEO-environmental Society
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• v.20 no.2
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• pp.69-79
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• 2019

The coastal area forms various sedimentary layers according to the environmental conditions such as the topography and geological features of the upper region of the river, ocean currents, and river mouth. Therefore, identifying the characteristics of the marine clay deposited in the coastal area plays a key role in the investigation of the formation of soft ground. In general, alluvial grounds are formed by a variety of factors such as changes in topography and natural environment, they have very diverse qualities depending on the deposited region or sedimentation conditions. The most important thing for the construction of social infrastructures in soft ground areas is economical and efficient treatment of soft ground. In this study, the author collected data from diverse laboratory and field tests on five areas in western and southern offshore with relatively high reliability, and then statistically analyzed them, thereby presenting standard constants for construction design. Correlation between design parameters such as over consolidation ratio, preconsolidation pressure was analyzed using linear and non-linear regression analyses. Also, proposed distribution characteristics of design parameters in consideration of each region's uncertainty through statistical analyses such as normality verification, outlier removal.

• Journal of Climate Change Research
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• v.4 no.4
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• pp.349-358
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• 2013

In this study, the newest technology available to reduce GHG emissions, which can be applicable in energy industries of the future that has large reduction obligations by energy target management and large intensity of GHG emissions, has been investigated by searching the technical characteristics of each technology. The newest technology to reduce GHG emissions in the field of power generation and energy can be mainly classified into the improvement of efficiency, CCS, and gas combined-cycle technology. In order to improve the reliability of the GHG emission factor obtained from the investigation process, it has been compared to the technology-specific GHG emission factor derived from the estimated amount of emissions. Then the GHG abatement measures, using the derived estimation of factor, by using the newest technology to reduce GHG emissions have been predicted. As a result, the GHG reduction rate by technology of CCS development has been expected to be the largest more than 30%, and the abatement rate by technology of coal gasified fuel cell and pressurized fluidized-bed thermal power generation has been showed more than 20%. If the effective introduction of the newest technology and the study of its characteristics is continued, and properly applied for future GHG emissions, it can be prospected that the national GHG reduction targets can be achieved in cost-efficient way.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.48-58
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• 2021

For securing technology and business competences of companies that is the engine of domestic industrial growth, government-supported policy programs for the creation of commercialization results in various forms such as 『Technology Transaction Market Vitalization』 and 『Technology Finance-based R&D Commercialization Support』 have been carried out since 2014. So far, various studies on technology valuation theories and evaluation variables have been formalized by experts from various fields, and have been utilized in the field of technology commercialization. However, Their practicality has been questioned due to the existing constraint that valuation results are assessed lower than the expectation in the evaluation sector. Even considering that the evaluation results may differ depending on factors such as the corporate situation and investment environment, it is necessary to establish a reference infrastructure to secure the objectivity and reliability of the technology valuation results. In this study, we investigate the evaluation infrastructure built by each institution and examine whether the latest artificial neural networks and deep learning technologies are applicable for performing predictive simulation of technology values based on principal variables, and predicting sales estimates and qualitative evaluation scores in order to embed onto the technology valuation system.

• Particle and aerosol research
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• v.16 no.4
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• pp.107-117
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• 2020

Based on the airborne measurement results over a coal fired power plant and steel work in Dangjin city, SO2 emission amounts of each site are estimated (top-down emission). Airborne measurements were carried out on May-June and October-November 2019. The estimated SO2 emission in 2019 for the power plant was 1502.1 kg/hr and that for the steel work was 2850.5 kg/hr, higher as much as a factor of 2.5 and 2.0, respectively, than the emission amounts provided by both facilities (bottom-up emission). The outcomes strongly illustrates that well designed airborne observations can serve a quantitative diagnostic tool for bottom-up emission estimates. Further research direction to improve the reliability of the top-down emission estimates is suggested.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.567-575
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• 2021

Residents in Dangjin, South Chungcheong Province, in which large-scale emissions facilities such as coal-fired power plants and steel mills are concentrated, are very much concerned about their health despite the local government's aggressive efforts to improve air quality and reduce greenhouse gases. To understand the impact of coal-fired power plants and external factors on local air pollution, the origins of local pollutants were investigated using stable carbon isotopes that are generally used as tracers of the provenance of fine or ultrafine dust. The origins of the pollutants were analyzed with the data library, built using the seasonally measured data for the two separate locations selected considering the distance from the coal-fired power plant and the analysis of previous studies, and with the back trajectory analysis. As a result of analyzing stable isotope ratios, the tendency of high concentration was found in the order of winter > spring > fall > summer. According to the data matching with the library, the mobile pollutants and open-air incineration had a relatively higher impact on the local air pollution. It is believed that this study, as a pilot study, should focus on securing the reliability of the study results through continuous monitoring and data accumulation.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.206-216
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• 2020

As the number of aging railway bridges in Korea increases, maintenance costs due to aging are increasing and continuous management is becoming more important. However, while the number of old facilities to be managed increases, there is a shortage of professional personnel capable of inspecting and diagnosing these old facilities. To solve these problems, this study presents an improved model that can detect Local damage to structures using machine learning techniques of AI technology. To construct a damage detection machine learning model, an analysis model of the bridge was set by referring to the design drawing of a non-ballasted plate-girder railroad bridge. Static strain data according to the damage scenario was extracted with the analysis model, and the Local damage index based on the reliability of the bridge was presented using statistical techniques. Damage was performed in a three-step process of identifying the damage existence, the damage location, and the damage severity. In the estimation of the damage severity, a linear regression model was additionally considered to detect random damage. Finally, the random damage location was estimated and verified using a machine learning-based damage detection classification learning model and a regression model.

• Journal of the Korean GEO-environmental Society
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• v.23 no.12
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• pp.5-15
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• 2022

Conventionally, because evaluation methods of the bearing capacity for double steel pipe-concrete composite pile design have not been established, the conventional vertical bearing capacity equations for steel hollow pile are used. However, there are severe differences between the predictions from these equations, and the most conservative one among vertical bearing capacity predictions are conventionally adopted as a design value. Consequently, the current prediction method for vertical bearing capacity of composite pile prediction composite pile causes design reliability and economical feasibility to be low. This paper investigated mechanical behaviors of a new composite pile, with a cross-section composed of double steel pipes filled with concrete (DSCT), vertical bearing capacities were analyzed for several DSCT pile conditions. Axisymmetric finite element models for DSCT pile and surrounding ground were created and they were used to analyze effects on behaviors of DSCT pile pile by embedding depth, stiffness of plugging material at pile tip, height of plugging material at pile tip, and rockbed material. Additionally, results from conventional design prediction equations for vertical bearing capacity at steel hollow pile tip were compared with that from numerical results, and the use of the conventional equations for steel hollow pile was examined to apply to that for DSCT pile.