• Journal of Korean Society for Atmospheric Environment
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• v.31 no.5
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• pp.430-436
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• 2015

Numerical air quality forecasting suffers from the large uncertainties of input data including emissions, boundary conditions, earth surface properties. Data assimilation has been widely used in the field of weather forecasting as a way to reduce the forecasting errors stemming from the uncertainties of input data. The present study aims at evaluating the effect of input data on the air quality forecasting results in Korea when data assimilation was invoked to generate the initial concentrations. The forecasting time was set to 36 hour and the emissions and initial conditions were chosen as tested input parameters. The air quality forecast model for Korea consisting of WRF and CMAQ was implemented for the test and the chosen test period ranged from November $2^{nd}$ to December $1^{st}$ of 2014. Halving the emission in China reduces the forecasted peak value of $PM_{10}$ and $SO_2$ in Seoul as much as 30% and 35% respectively due to the transport from China for the no-data assimilation case. As data assimilation was applied, halving the emissions in China has a negligible effect on air pollutant concentrations including $PM_{10}$ and $SO_2$ in Seoul. The emissions in Korea still maintain an effect on the forecasted air pollutant concentrations even after the data assimilation is applied. These emission sensitivity tests along with the initial condition sensitivity tests demonstrated that initial concentrations generated by data assimilation using field observation may minimize propagation of errors due to emission uncertainties in China. And the initial concentrations in China is more important than those in Korea for long-range transported air pollutants such as $PM_{10}$ and $SO_2$. And accurate estimation of the emissions in Korea are still necessary for further improvement of air quality forecasting in Korea even after the data assimilation is applied.

• Journal of Environmental Science International
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• v.24 no.7
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• pp.865-874
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• 2015

This study was conducted to investigate the correlation between the distribution chart and input data of the predicted 3-second gust and damage cost, by using the forecast field and analysis field of Regional Data Assimilation Prediction System (RDAPS) as initial input data of Korea risk assessment model (RAM) developed in the preceding study. In this study the cases of typhoon Rusa which caused occurred great damage to the Korean peninsula was analyzed to assess the suitability of initial input data. As a result, this study has found out that the distribution chart from the forecast field and analysis field predicted from the point where the effect due to the typhoon began had similarity in both 3-second gust and damage cost with the course of time. As a result of examining the correlation, the 3-second gust had over 0.8, and it means that the forecast field and analysis field show similar results. This study has shown that utilizing the forecast field as initial input data of Korea RAM could suit the purpose of pre-disaster prevention.

• Spatial Information Research
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• v.20 no.1
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• pp.27-38
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• 2012

The recent development of CFD techniques are being involved w ith Environmental Impact Assessment and Environmental DesignroThey are being applied to the Site Planning and Engineering Design works as a new trendroHowever, CFD laboratory works are not extended to the field works in Industrial Project due to inaccuracy of the data input process that is cause by absence of regional height informationsroHence, in this study, we promote to build a new initial input data processing steps and algorithms for CFD Model generation. ENVI-met model is very popular, efficient, and freely downloadable CFD model. Light Detection And Ranging (LiDAR) are well known state of art technology and dataset proving a reliable accuracy for CFD. We use LiDAR data as a input source for CFD input producing process and algorithm development and evaluation. CFD initial input data generation process and results derived from am development and set is very useful and efficient for rapid CFD input data producing and maklomore reliable CFD Model forec st for atmospheric and climatic analysis for planning and design engineering industry.

• Korean Journal of Computational Design and Engineering
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• v.7 no.3
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• pp.157-169
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• 2002

In the initial stages of ship design, designers represent geometry, arrangement, and dimension of hull structures with 2D geometric primitives such as points, lines, arcs, and drawing symbols. However, these design information(‘2D geometric primitives’) defined in the drawing sheet require more intelligent translation processes by the designers in the next design stages. Thus, the loss of design semantics could be occurred and following design processes could be delayed. In the initial design stages, it is not easy to adopt commercial 3D CAD systems, which have been developed f3r being used in detail and production design stages, because the 3D CAD systems require detailed input for geometry definition. In this study, a semantic product model data structure was proposed, and an initial structural CAD system was developed based on the proposed data structure. Contents(‘product model data and design knowledges’) of the proposed data structure are filled with minimal input of the designers, and then 3D solid model and production material information can be automatically generated as occasion demands. Finally, the applicability of the proposed semantic product model data structure and the developed initial structural CAD system was verified through application to deadweight 300,000ton VLCC(Very Large Crude oil Carrier) product modeling procedure.

• International Journal of CAD/CAM
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• v.11 no.1
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• pp.18-26
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• 2011

This paper proposes a new solid-shape modeling system based on a lusterware-image illustration. The proposed method reconstructs a three dimensional solid shape from a set of rough sketches that are typically drawn in the early stages of the design process. The sketches do not have to be strictly accurate, and this tolerance to the roughness of the input sketches is one of the major advantages of the proposed method. The proposed system creates an initial shape based on the silhouette of the input lusterware-images. Then the user can edit the initial shape with intuitive cutting and dishing-up operations, which are based on sketching user interface. To achieve the goal, the system retains the geometric model with two representations: a point-set data and a volume data. This dual data structure allows the program to create an initial shape from the input images with little computational cost, and the user can apply cutting and dishing-up operations without substantially increasing computational and memory requirements. In this research, we have tested the proposed system by reconstructing solid models of some mechanical parts from rough sketches. The experimental results indicate that the proposed method is useful for the prototyping of a solid shape.

• Journal of Environmental Science International
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• v.33 no.6
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• pp.435-442
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• 2024

We investigated the accuracy of surface air temperature prediction according to the selection of land-use data and initial meteorological data using the Weather Research and Forecasting model-v4.2.1. A numerical experiment was conducted at the Daegu Dyeing Industrial Complex. We initially used meteorological input data from GFS (Global forecast system)and GDAPS (Global data assimilation and prediction system). High-resolution input data were generated and used as input data for the weather model using the land cover data of the Ministry of Environment and the digital elevation model of the Ministry of Land, Infrastructure, and Transport. The experiment was conducted by classifying the terrestrial and topographic data (land cover data) and meteorological data applied to the model. For simulations using high-resolution terrestrial data(10 m), global data assimilation, and prediction system data(CASE 3), the calculated surface temperature was much closer to the automatic weather station observations than for simulations using low-resolution terrestrial data(900 m) and GFS(CASE 1).

• Proceedings of the Korean Society for Quality Management Conference
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• 2010.04a
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• pp.226-234
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• 2010

In this thesis, we present process that remove mistake of data before statistical analysis. If field data which is not simple examination about validity of data, we cannot believe analyzed statistics information. As statistical analysis information is produced based on data to be input in statistical analysis process, the data to be input should be free of error. In this paper, we study the application of statistical analysis road map that can enhance application on site by organizing basic theory and approaching on initial data exploratory phase, essential step before conducting statistical analysis. Therefore, access to statistical analysis can be enhanced and reliability on result of analysis can be secured by conducting correct statistical analysis.

• Language and Information
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• v.9 no.1
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• pp.69-92
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• 2005

Pattern recognition in cognitive agents is based on (i) the uninterpreted input data (e.g. parameter values) provided by the agent's hardware devices and (ii) and interpreted patterns (e.g. templates) provided by the agent's memory. Computationally, the task consists in finding the memory data corresponding best to the input data, for any given input. Once the best fitting memory data have been found, the input is recognized by applying to it the interpretation which happens to be stored with the memorized pattern. This paper presents a fast converging procedure which starts from a few initially recognized items and then analyzes the remainder of the input by systematically checking for items shown by memory to have been related to the initial items in previous encounters. In this way, known patterns are tried first, and only when they have been exhausted, an elementary exploration of the input is commenced. Efficiency is improved further by choosing the candidate to be tested next according to frequency.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.9-11
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• 2005

We propose a optimal identification of information granulation(IG)-based fuzzy model to carry out the model identification of complex and nonlinear systems. To optimally identity we use genetic algorithm (GAs) sand Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the selected input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• Computers and Concrete
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• v.15 no.1
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• pp.55-71
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• 2015

This paper presents a novel harmony search (HS)-based data-driven single input rule modules (SIRMs)-connected fuzzy inference system (FIS) for the prediction of stress in externally prestressed tendon. The proposed method attempts to extract causal relationship of a system from an input-output pairs of data even without knowing the complete physical knowledge of the system. The monotonicity property is then exploited as an additional qualitative information to obtain a meaningful SIRMs-connected FIS model. This method is then validated using results from test data of the literature. Several parameters, such as initial tendon depth to beam ratio; deviators spacing to the initial tendon depth ratio; and distance of a concentrated load from the nearest support to the effective beam span are considered. A computer simulation for estimating the stress increase in externally prestressed tendon, ${\Delta}f_{ps}$, is then reported. The contributions of this paper is two folds; (i) it contributes towards a new monotonicity-preserving data-driven FIS model in fuzzy modeling and (ii) it provides a novel solution for estimating the ${\Delta}f_{ps}$ even without a complete physical knowledge of unbonded tendons.