• Journal of Korean Society of Coastal and Ocean Engineers
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• 제34권6호
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• pp.222-232
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• 2022

In this study we investigated the characteristics of long-term tidal constituents based on 30 tidal gauge data along the coasts of Korea and its the effects on total water level (TWL) forecasts. The results show that the solar annual (Sa) and semiannual (Ssa) tides were dominant among long-term tidal constituents, and they are relatively large in western coast of Korea peninsula. To investigate the effect of long-term tidal constituents on TWL forecasts, we produced predicted tides in 2021 with and without long-term tidal constituents. The TWL forecasts with and without long-term tidal constituents are then calculated by adding surge forecasts into predicted tides. Comparing with the TWL without long-term tidal constituents, the results with long-term tidal constituents reveals small bias in summer and relatively large negative bias in winter. It is concluded that the large error found in winter generally caused by double-counting of meteorological factors in predicted tides and surge forecasts. The predicted surge for 2021 based on the harmonic analysis shows seasonality, and it reduces the large negative bias shown in winter when it subtracted from the TWL forecasts with long-term tidal constituents.

• Journal of Korean Society of Forest Science
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• 제112권3호
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• pp.374-381
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• 2023

The travel time of flash floods along mountain streams is mainly governed by reach-average velocity, rather than by the point velocity of the locations of interest. Reach-average velocity is influenced by various factors such as stream geometry, streambed materials, and the hydraulic roughness of streams. In this study, the reach-average velocity in mountain streams was measured for storm periods using rhodamine dye tracing. The point cloud data obtained from a LiDAR survey was used to extract the average hydraulic roughness height, such as R_a, R_max, and R_z. The size distribution of the streambed materials (D₅₀, D₈₄) was also considered in the estimation of the roughness height. The field experiments revealed that the reach-average velocities had a significant relationship with flow discharges (v = 0.5499Q^0.6165 ), with an R² value of 0.77. The root mean square error in the roughness height of the R_a-based estimation (0.45) was lower than those of the other estimations (0.47-1.04). Among the parameters for roughness height estimation, the R_a -based roughness height was the most reliable and suitable for developing the reach-average velocity equation for estimating the travel time of flood waves in mountain streams.

• Journal of IKEEE
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• 제27권3호
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• pp.345-349
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• 2023

In this paper, we propose a study on the development of deep learning structure for defective pixel detection of next-generation smart LED display board using imaging device. In this research, a technique utilizing imaging devices and deep learning is introduced to automatically detect defects in outdoor LED billboards. Through this approach, the effective management of LED billboards and the resolution of various errors and issues are aimed. The research process consists of three stages. Firstly, the planarized image data of the billboard is processed through calibration to completely remove the background and undergo necessary preprocessing to generate a training dataset. Secondly, the generated dataset is employed to train an object recognition network. This network is composed of a Backbone and a Head. The Backbone employs CSP-Darknet to extract feature maps, while the Head utilizes extracted feature maps as the basis for object detection. Throughout this process, the network is adjusted to align the Confidence score and Intersection over Union (IoU) error, sustaining continuous learning. In the third stage, the created model is employed to automatically detect defective pixels on actual outdoor LED billboards. The proposed method, applied in this paper, yielded results from accredited measurement experiments that achieved 100% detection of defective pixels on real LED billboards. This confirms the improved efficiency in managing and maintaining LED billboards. Such research findings are anticipated to bring about a revolutionary advancement in the management of LED billboards.

• Journal of Environmental Impact Assessment
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• 제32권4호
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• pp.203-213
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• 2023

The AERMOD model was the most used, accounting for 89.0%, based on the analysis of the environmental impact assessment reports published in the Environmental Impact Assessment Information Support System (EIASS) between 2021 and 2022. The mismatch of versions between AERMET and AERMOD was found to be 25.3%. There was the operational time discrepancy of 50.6% from industrial complexes, urban development projects between used in the model and applied in estimating pollutant emissions. The results of applying various versions of the AERMET and AERMOD models to both area sources and point sources in both simple and complex terrain in the Gunsan area showed similar values after AERMOD version 12 (15181). Emissions are assessed as 24-hour operation, and the predicted concentration in both simple and complex terrain when using the variable emission coefficient option that applies an 8-hour daytime operation in the model is lowered by 37.42% ~ 74.27% for area sources and by 32.06% ~ 54.45% for point sources. Therefore, to prevent the error in using the variable emission coefficient, it is required to clearly present the emission calculation process and provide a detailed explanation of the composition of modeling input data in the environmental impact assessment reports. Also, thorough reviews by special institutions are essential.

• Korean Journal of Construction Engineering and Management
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• 제24권5호
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• pp.22-34
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• 2023

Predicting accurately the construction cost budget in the early stages of construction projects is crucial to support the client's decision-making and achieve the objectives of the construction project. This holds true for public construction projects as well. However, the current methods for predicting construction cost budgets in the early stages of public construction projects are not sophisticated enough in terms of accuracy and reliability, indicating a need for improvement. The objective of this study is to develop a construction cost budget prediction model that can be utilized in the early stages of public building projects using an artificial neural network (ANN). In this study, an artificial neural network model was developed using the SPSS Statistics program and the data provided by the Public Procurement Service. The level of construction cost budget prediction was analyzed, and the accuracy of the model was validated through additional testing. The validation results demonstrated that the developed artificial neural network model exhibited an error range for estimates that can be utilized in the early stages of projects, indicating the potential to predict construction cost budgets more accurately by incorporating various project conditions.

• 한국정책학회보
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• 제21권4호
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• pp.199-228
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• 2012

Policy problems are complex due to diverse participants and their relations in the policy processes. Defining the right problem in the first place is important because Type III error is likely to happen without removing rival hypothesis in defining the problem. This study applies Boundary Analysis suggested by Dunn to structure IT policy problems in Korea. The time frame of the study focuses on 5 years of Lee Administration and data are collected from four newspapers. Using content analysis, the study, first, elaborates total 2,614 policy problems from 1,908 stakeholders. After removing duplicating problems, 369 problems from 323 stakeholders are identified as a boundary of IT policy problem. Among others, failures in government policies are weighted as the most serious problems in IT policy field. However, many significant problems raised by stakeholders dated back to more than a decade, and those are intrinsic problems, which initially caused by market distortions in the IT industry. Therefore, we should be cautious not to overemphasize the most conspicuous problem as the only problem in the policy field when we interpret results of problem structuring.

• Journal of Broadcast Engineering
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• 제28권3호
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• pp.285-292
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• 2023

Recently, the impact of fine dust on health has become a major topic. Fine dust is dangerous because it can penetrate the body and affect the respiratory system, without being filtered out by the mucous membrane in the nose. Since fine dust is directly related to the industry, it is practically impossible to completely remove it. Therefore, if the concentration of fine dust can be predicted in advance, pre-emptive measures can be taken to minimize its impact on the human body. Fine dust can travel over 600km in a day, so it not only affects neighboring areas, but also distant regions. In this paper, wind direction and speed data and a time series prediction model were used to predict the concentration of fine dust in Seoul, and the correlation between the concentration of fine dust in Seoul and the concentration in each region was confirmed. In addition, predictions were made using the concentration of fine dust in each region and in Seoul. The lowest MAE (mean absolute error) in the prediction results was 12.13, which was about 15.17% better than the MAE of 14.3 presented in previous studies.

• Journal of the Korean Geotechnical Society
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• 제24권9호
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• pp.41-48
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• 2008

The aim of this study is to present a graphical method in order to evaluate stages in shrinkage cracking. Firstly, the distribution of crack openings is established by sorting the openings of individual cracks in the soil cracking system. Secondly, it is normalized in a range of 0 to 1 to obtain the normalized crack opening distribution. Thirdly, three S-shape curve models introduced by Brooks and Corey(1964), Fredlund and Xing(1994) and van Genuchten(1980) are chosen to fit the normalized crack opening distribution using a curve fitting method. The accuracy of fitting which is described through fitting parameters by the van Genuchten equation is much higher than that by the Brooks and Corey equation and slightly higher than that by the Fredlund and Xing equation; thus the van Genuchten model is used. Finally, the stages of shrinkage cracking are graphically evaluated by drawing three separate straight lines corresponding to three linear parts of the fitted normalized crack opening distribution. The proposed method is tested with different sample thicknesses. The measured data are fitted by the selected model with the fairly high regression coefficient and small root mean square error. The results show graphically that shrinkage cracking comprises three stages; namely, primary, secondary and residual stages. Subsequently, the ranges of evaluated crack opening for each of these stages are presented.

• Nuclear Engineering and Technology
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• 제55권1호
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• 제40권1호
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• pp.51-66
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• 2022

SST (Sea Surface Temperature) is based on the atmosphere-ocean interaction, one of the most important mechanisms for the Earth system. Because it is a crucial oceanic and meteorological factor for understanding climate change, gap-free grid data at a specific spatial and temporal resolution is beneficial in SST studies. This paper examined the production of daily SST grid maps from 137 stations in 2020 through the ordinary kriging with variogram optimization and their accuracy assessment. The variogram optimization was achieved by WLS (Weighted Least Squares) method, and the blind tests for the interpolation accuracy assessment were conducted by an objective and spatially unbiased sampling scheme. The four-round blind tests showed a pretty high accuracy: a root mean square error between 0.995 and 1.035℃ and a correlation coefficient between 0.981 and 0.982. In terms of season, the accuracy in summer was a bit lower, presumably because of the abrupt change in SST affected by the typhoon. The accuracy was better in the far seas than in the near seas. West Sea showed better accuracy than East or South Sea. It is because the semi-enclosed sea in the near seas can have different physical characteristics. The seasonal and regional factors should be considered for accuracy improvement in future work, and the improved SST can be a member of the SST ensemble around South Korea.