• Journal of the Korea Convergence Society
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• v.8 no.6
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• pp.29-36
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• 2017

In this paper, we propose a method for generating improved saliency map by learning multiple features to improve the accuracy and reliability of saliency map which has similar result to human visual perception type. In order to overcome the inaccurate result of reverse selection or partial loss in color based salient area estimation in existing salience map generation, the proposed method generates multi feature data based on learning. The features to be considered in the image are analyzed through the process of distinguishing the color pattern and the region having the specificity in the original image, and the learning data is composed by the combination of the similar protrusion area definition and the specificity area using the LAB color space based color analysis. After combining the training data with the extrinsic information obtained from low level features such as frequency, color, and focus information, we reconstructed the final saliency map to minimize the inaccurate saliency area. For the experiment, we compared the ground truth image with the experimental results and obtained the precision-recall value.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.4
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• pp.246-251
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• 2017

Crop models have been used to predict a heading date for efficient management of fertilizer application. Recently, the ORYZA (v3) model was developed to improve the ORYZA2000 model, which has been used for simulation of rice growth in Korea. Still, little effort has been made to assess applicability of the ORYZA (v3) model to rice farms in Korea. The objective of this study was to evaluate reliability of heading dates predicted using the the ORYZA (v3) model, which would indicate applicability of the model to a decision support system for fertilizer application. Field experiments were conducted from 2015-2016 at the Rural Development Administration (RDA) to obtain rice phenology data. Shindongjin cultivar which is mid-late maturity type was grown under a conventional fertilizer management, e.g., application of fertilizer at the rate of 11 Kg N/10a. Another set of heading dates was obtained from annual reports at experiment farms operated by the National Institute of Crop Science and Agricultural Technology Centers in each province. The input files for the ORYZA (v3) model were prepared using weather and soil data collected from the Korean Meteorology Administration (KMA) and the Korean Soil Information System, respectively. Input parameters for crop management, e.g., transplanting date and planting density, were set to represent management used for the field experiment. The ORYZA (v3) model predicted heading date within 1 day for two seasons. The crop model also had a relatively small error in prediction of heading date for three ecotypes of rice cultivars at experiment farms where weather input data were obtained from a near-by weather station. Those results suggested that the ORYZA (v3) model would be useful for development of a decision support system for fertilizer application when reliable input data for weather variables become available.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.2
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• pp.68-78
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• 2020

Crop models have been used to predict vegetable crop yield, which would have a considerable economic impact on consumers as well as producers. A small number of models have been developed to estimate growth and yield of vegetables due to limited availability of growth observation data in high-quality. In this study, we aimed to analyze the protocols designed for collection of the observation data for major vegetable crops including cabbage, radish, garlic, onion and pepper. We also designed the protocols suitable for development and verification of a vegetable crop growth model. In particular, different measures were proposed to improve the existing protocol used by Statistics Korea (KOSTAT) and Rural Development Administration (RDA), which would enhance reliability of parameter estimation for the crop model. It would be advantageous to select sampling sites in areas where reliable weather observation data can be obtained because crop models quantify the response of crop growth to given weather conditions. It is recommended to choose multiple sampling sites where climate conditions would differ. It is crucial to collect time series data for comparison between observed and simulated crop growth and yield. A crop model can be developed to predict actual yield rather than attainable yield using data for crop damage caused by diseases and pests as well as weather anomalies. A bigdata platform where the observation data are to be shared would facilitate the development of crop models for vegetable crops.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.721-731
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• 2010

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.

• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.57-66
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• 2006

This paper presents new pedestrian signal timings considering pedestrian demand Pedestrian characteristics, and land use which were obtained by Pedestrian characteristics field survey and pedestrian signal operation survey. Pedestrian signal timings suggested were compared to the existing pedestrian signal timings by using real field data. pedestrian characteristics field survey was conducted to collect pedestrian crossing speed data and reaction time data. Sixteen areas in Seoul were selected for the data collection. The average pedestrian crossing speed was 1.30m/sec and the 15th Percentile speed was 1.11m/sec. The average reaction time was 2.24 seconds. Pedestrian crossing speed differs by land use, road width. pedestrian age, sex, and number of Pedestrians. Reaction time also differs by road width, pedestrian age, and number of pedestrians. Statistical testing was performed to secure reliability of the collected data.

• 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.