• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1413-1424
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• 2013

Recently, the frequency of severe storms increases in Korea. Severe storms occurring in a short time cause huge losses of both life and property. A considerable research has been performed for the flood control system development based on an accurate stream discharge prediction. A physical model is mainly used for flood forecasting and warning. Physical rainfall-runoff models used for the conventional flood forecasting process require extensive information and data, and include uncertainties which can possibly accumulate errors during modelling processes. ANFIS, a data driven model combining neural network and fuzzy technique, can decrease the amount of physical data required for the construction of a conventional physical models and easily construct and evaluate a flood forecasting model by utilizing only rainfall and water level data. A data driven model, however, has a disadvantage that it does not provide the mathematical and physical correlations between input and output data of the model. The characteristics of a data driven model according to functional options and input data such as the change of clustering radius and training data length used in the ANFIS model were analyzed in this study. In addition, the applicability of ANFIS was evaluated through comparison with the results of HEC-HMS which is widely used for rainfall-runoff model in Korea. The neuro-fuzzy technique was applied to a Cheongmicheon Basin in the South Han River using the observed precipitation and stream level data from 2007 to 2011.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.34 no.6
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• pp.617-623
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• 2001

To predict the seasonal variation of dissolved oxygen (DO) in Hakata bay, Japan, possible 20 time-series of different hourly-solar-radiation intensities were generated based on stochastically changing solar radiation intensity, and a numerical simulation on dissolved oxygen (DO) was carried out for each time series by using the Sediment-Water Ecological Model (SWEM). The model, consisting of two sub-models with hydrodynamic and biological models, simulates the circulation process of nutrient between water column and sediment, such as nutrient regeneration from sediments as well as ecological structures on the growth of phytoplankton and zooplankton, The results of the model calibration followed the seasonal variation of observed water quality well, and generated cumulative-frequency-distribution (CFD) curves of daily solar radiation agreed well with observed ones, The simulation results indicated that the exchange of sea water would have a great influence on the DO concentration, and that the concentration could change more than 1 mg/L in a day. This prediction method seems to be an effective way to examine a solution to minimize fishery damage when DO is depleted.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.6
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• pp.54-63
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• 2014

Traffic accident frequency and severity level in Korea are known to be very serious. Especially the number of pedestrian fatalities was much worse and 1.6 time higher than the OECD average. According to the National Police Agency, the flash signals are reported to have many safety benefits as well as travel time reduction, which is opposed to the foreign studies. With this background of expanding the flash signal, this research aims to investigate the overall impact of the flash signal operation on safety, investigating and comparing the accident occurrence on the flash signal and the full signal intersections. For doing this accident prediction models for both flash and full signal intersections were estimated using independent variables (geometric features and traffic volume) and 3-year (2011-2013) accident data collected in Seoul. Considering the rare and random nature of accident occurrence and overdispersion (variance > mean) of the data, the negative binomial regression model was applied. As a result, installing wider crosswalk and increasing the number of pedestrian push buttons seemed to increase the safety of the flash signal intersections. In addition, the result showed that the average accident occurrence at the flash signal intersections was higher than at the full signal-operated intersections, 9% higher with everything else the same.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.12
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• pp.1292-1299
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• 2014

Weather radar systems can provide weather information of the ground, sea, and air in extensive spatial coverage in near real time. However, it becomes problematic when ground clutter signal exists around precipitation because strong signals of ground can cause a false precipitation report. A large percentage of land coverage of Korea consists of mountainous regions where ground clutter needs to be mitigated for more accurate prediction. Thus, it is considered necessary to introduce a new suitable ground clutter removal technique specifically adequate for Korea. In this paper, the C-Map(Clutter Map) method using raw radar signals is proposed for removing ground clutter using a terrain-adaptive clutter map. A clutter map is generated using raw radar signals(I/Q) of clear days, then it is subtracted from received radar signals in frequency domain. The proposed method is applied to the radar data acquired from Sobaeksan rain radar and the result shows that the clutter rejection ratio is about 91.17 %.

• Journal of the Korean earth science society
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• v.31 no.7
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• pp.749-761
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• 2010

A relationship between cloud-to-ground lightning and rainfall was investigated by using the two-years (2006-2007) summer lightning data and the automatic weather stations (AWSs) data of the Korea Meteorological Administration. The negative lightning occurred at the core of highly concentrated convection, which is often accompanied with heavy rains. Whereas most positive lightning occurred at the anvil cloud with low density and light rains. The rainfall intensity is strongest when the negative and positive lightning occurred concurrently, and one with lightning is much stronger than that without lightning. A portion of the positive lightning of the total lightning was less than 10% during summer seasons, and the lightning without rains was about 34%. The rain rate was strongly correlated with the negative flash rate, and the correlation coefficients varied between 0.87 and 0.94 according to the co-location radius (5-15 km) of AWSs. Most of the lightning occurred 10 minutes before and/or concurrently occurred with rains. A portion of the convective rainfalls of the total rainfalls was at least 20% when we define the rainfalls with lightning as convective. The convective rainfall was greater during August than in June and July. In general, the portion of convective rainfalls showed a maximum diurnal variation during late afternoon as in the rains and lightning.

• Journal of Korea Water Resources Association
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• v.44 no.8
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• pp.667-681
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• 2011

The objective of this study is to evaluate the applicability of a Land Surface Model (LSM) for drought analysis in Korea. For evaluating the applicability of the model, the model was calibrated on several upper dam site watersheds and the hydrological components (runoff and soil moisture) were simulated over the whole South Korea at grid basis. After converting daily series of runoff and soil moisture data to accumulated time series (3, 6, 12 months), drought indices such as SRI and SSI are calculated through frequency analysis and standardization of accumulated probability. For evaluating the drought indices, past drought events are investigated and drought indices including SPI and PDSI are used for comparative analysis. Temporal and spatial analysis of the drought indices in addition to hydrologic component analysis are performed to evaluate the reproducibility of drought severity as well as relieving of drought. It can be concluded that the proposed indices obtained from the LSM model show good performance to reflect the historical drought events for both spatially and temporally. From this point of view, the LSM can be useful for drought management. It leads to the conclusion that these indices are applicable to domestic drought and water management.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.201-208
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• 2012

In this paper, the hardware design of rate control for real-time video encoded is proposed. In the proposed method, a quadratic rate distortion model with high-computational complexity is not used when quantization parameter values are being decided. Instead, for low-computational complexity, average complexity weight values of frames are used to calculate QP. For high speed and low computational prediction, the MAD is predicted based on the coded basic unit, using spacial and temporal correlation in sequences. The rate control is designed with the hardware for fast QP decision. In the proposed method, a quadratic rate distortion model with high-computational complexity is not used when quantization parameter values are being decided. Instead, for low-computational complexity, average complexity weight values of frames are used to calculate QP. In addition, the rate control is designed with the hardware for fast QP decision. The execution cycle and gate count of the proposed architecture were reduced about 65% and 85% respectively compared with those of previous architecture. The proposed RC was implemented using Verilog HDL and synthesized with UMC $0.18{\mu}m$ standard cell library. The synthesis result shows that the gate count of the architecture is about 19.1k with 108MHz clock frequency.

• The Journal of the Acoustical Society of Korea
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• v.35 no.6
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• pp.480-490
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• 2016

As wind turbines become larger and lighter, they are likely to respond sensitively by dynamic loads applied on them. Since the responses at resonances are particularly interested, it is required to be able to predict natural frequencies of wind turbines reliably at early design stage. To achieve this, the foundation-soil analysis is needed to be carried out and a finite element approach is adopted in general. However, the finite element approach would not be appropriate in early design stage because it demands heavy efforts in pile-soil modelling and computing facilities. On the contrary, theoretical approaches adopting linear approximations for soils are relatively simple and easy to handle. Therefore, they would be a useful tool in predicting a pile-soil interaction, particularly in early design stage. In this study an analysis for a pile inserted in soil is performed. The pile and soil are modelled as a beam and continuum medium, respectively, within an elastic range. In this analysis, influence factors at the pile head for lateral loads are predicted by means of this continuum approach for various length-diameter ratios of the pile. The influence factors predicted are validated with those reported in literature, proposed from a finite element analysis.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Journal of Wetlands Research
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• v.26 no.3
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• pp.197-203
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• 2024

The algal blooms in rivers can negatively affect water source management and water treatment processes, necessitating continuous management. In this study, a multi-classification model was developed to predict the concentration of chlorophyll-a (chl-a), one of the key indicators of algal blooms, using Tabular Prior Fitted Networks (TabPFN), a novel deep learning algorithm known for its relatively superior performance on small tabular datasets. The model was developed using daily observation data collected at Buyeo water quality monitoring station from January 1, 2014, to December 31, 2022. The collected data were averaged to construct input data sets with measurement frequencies of 1 day, 3 days, 6 days, 12 days. The performance comparison of the four models, constructed with input data on observation frequencies of 1 day, 3 days, 6 days, and 12 days, showed that the model exhibits stable performance even when the measurement frequency is longer and the number of observations is smaller. The macro average for each model were analyzed as follows: Precision was 0.77, 0.76, 0.83, 0.84; Recall was 0.63, 0.65, 0.66, 0.74; F1-score was 0.67, 0.69, 0.71, 0.78. For the weighted average, Precision was 0.76, 0.77, 0.81, 0.84; Recall was 0.76, 0.78, 0.81, 0.85; F1-score was 0.74, 0.77, 0.80, 0.84. This study demonstrates that the chl-a prediction model constructed using TabPFN exhibits stable performance even with small-scale input data, verifying the feasibility of its application in fields where the input data required for model construction is limited.