• Journal of the Korean GEO-environmental Society
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• v.7 no.5
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• pp.79-87
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• 2006

This paper presents the numerical model developed to simulate the solute transport in rough and smooth single fractures. The roughness of these fractures is represented by using the fractal surface method. In this study, the 3D transport model, which is based on the random walk technique, is used to simulate the dispersion process of a solute which is represented by numerical particles. As the simulation results, it can be observed that the dispersion of solute in the fracture is significantly affected by the fracture roughness and particle size.

• Journal of the Korean Chemical Society
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• v.50 no.4
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• pp.277-280
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• 2006

We report an exact relation between the survival probability, the revisit time distribution, and the reaction-free propagator of the continuous time random walker. The relation holds even for such a general case where the random walker has a distinct jump dynamics at each lattice site, which may be dependent also on the direction of the jump. The application range of the obtained relation is not limited to the nearest neighbor hopping in the bulk lattice either. The result is applicable to a higher dimensional system with the spherical symmetry as well as it is to the one-dimensional system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.191-197
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• 2015

In this paper, we propose the system which recognize the gaze using Random Forests in vehicular environment based on smart-phone. Proposed system is mainly composed of the following: face detection using Adaboost, face component estimation using Histograms, and gaze recognition based on Random Forests. We detect a driver based on the image information with a smart-phone camera, and the face component of driver is estimated. Next, we extract the feature vectors from the estimated face component and recognize gaze direction using Random Forest recognition algorithm. Also, we collected gaze database including a variety gaze direction in real environments for the experiment. In the experiment result, the face detection rate and the gaze recognition rate showed 82.02% and 84.77% average accuracies, respectively.

• Journal of Korean Society on Water Environment
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• v.35 no.5
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• pp.425-431
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• 2019

Recently, people have been attracting attention to the good quality of water resources as well as water welfare. to improve the quality of life. This study is a papers on the prediction of benthic macroinvertebrate index (BMI), which is a aquatic ecological health, using the machine learning based CFS (Correlation-based Feature Selection) method and the random forest model to compare the measured and predicted values of the BMI. The data collected from the Han River's branch for 10 years are extracted and utilized in 1312 data. Through the utilized data, Pearson correlation analysis showed a lack of correlation between single factor and BMI. The CFS method for multiple regression analysis was introduced. This study calculated 10 factors(water temperature, DO, electrical conductivity, turbidity, BOD, $NH_3-N$, T-N, $PO_4-P$, T-P, Average flow rate) that are considered to be related to the BMI. The random forest model was used based on the ten factors. In order to prove the validity of the model, $R^2$, %Difference, NSE (Nash-Sutcliffe Efficiency) and RMSE (Root Mean Square Error) were used. Each factor was 0.9438, -0.997, and 0,992, and accuracy rate was 71.6% level. As a result, These results can suggest the future direction of water resource management and Pre-review function for water ecological prediction.

• Journal of Korean Society on Water Environment
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• v.35 no.5
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• pp.432-438
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• 2019

The purpose of this study is to predict Trophic Diatom Index (TDI) in tributaries of the Han River watershed using the random forest algorithm. The one year (2017) and supplied aquatic ecology health data were used. The data includes water quality(BOD, T-N, $NH_3-N$, T-P, $PO_4-P$, water temperature, DO, pH, conductivity, turbidity), hydraulic factors(water width, average water depth, average velocity of water), and TDI score. Seven factors including water temperature, BOD, T-N, $NH_3-N$, T-P, $PO_4-P$, and average water depth are selected by the Correlation Feature Selection. A TDI prediction model was generated by random forest using the seven factors. To evaluate this model, 2017 data set was used first. As a result of the evaluation, $R^2$, % Difference, NSE(Nash-Sutcliffe Efficiency), RMSE(Root Mean Square Error) and accuracy rate show that this model is compatible with predicting TDI. To be more concrete, $R^2$ is 0.93, % Difference is -0.37, NSE is 0.89, RMSE is 8.22 and accuracy rate is 70.4%. Also, additional evaluation using data set more than 17 times the measured point was performed. The results were similar when the 2017 data set were used. The Wilcoxon Signed Ranks Test shows there was no statistically significant difference between actual and predicted data for the 2017 data set. These results can specify the elements which probably affect aquatic ecology health. Also, these will provide direction relative to water quality management for a watershed that must be continuously preserved.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.5
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• pp.450-456
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• 2006

Monte Carlo localization is known to be one of the most reliable methods for pose estimation of a mobile robot. Although MCL is capable of estimating the robot pose even for a completely unknown initial pose in the known environment, it takes considerable time to give an initial pose estimate because the number of random samples is usually very large especially for a large-scale environment. For practical implementation of MCL, therefore, a reduction in sample size is desirable. This paper presents a novel approach to reducing the number of samples used in the particle filter for efficient implementation of MCL. To this end, the topological information generated through the thinning technique, which is commonly used in image processing, is employed. The global topological map is first created from the given grid map for the environment. The robot then scans the local environment using a laser rangefinder and generates a local topological map. The robot then navigates only on this local topological edge, which is likely to be similar to the one obtained off-line from the given grid map. Random samples are drawn near the topological edge instead of being taken with uniform distribution all over the environment, since the robot traverses along the edge. Experimental results using the proposed method show that the number of samples can be reduced considerably, and the time required for robot pose estimation can also be substantially decreased without adverse effects on the performance of MCL.

• The KIPS Transactions:PartA
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• v.9A no.3
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• pp.295-300
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• 2002

In this paper, we apply the 3-dimension algorithm to the random interleaver and s-random interleaver and analyze the performance of the turbo code system with random interleaver (or s-random interleaver). In general, the performance of interleaver is determined by minimum distance between neighbor data, thus we could improve the performance of interleaver by increasing the distance of the nearest data. The interleaver using 3-dimension algorithm has longer minimum distance and average distance compared to existing random-interleaver (s-random interleaver) because the output data is generated randomly from 3-dimension storage. To verify and compare the performance of our proposed system, the computer simulations have been performed in turbo code system under gaussian noise environment.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

• Journal of IKEEE
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• v.26 no.1
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• pp.89-94
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• 2022

The W-band (75-110GHz) is a band that can utilize at least 10 times more bandwidth than the existing 5G band. Therefore, it is one of the bands suitable for future mobile communication that requires high speed and low latency, such as virtual and augmented reality. However, since the wavelength is short, it has a high path loss and is very sensitive to the atmospheric environment. Therefore, in order to develop a W-band communication system in the future, it is necessary to analyze the characteristics of path loss according to the channel environment. In this paper, to analyze the characteristics of the W-band path loss, the random forest technique was used, and the influence of the channel parameters according to the distance section was analyzed through the path loss data according to various channel environment parameters. As a result of the simulation, the distance has the highest influence on the path loss in the short distance, and the other channel environment factor is almost ignored. However, as the distance section became longer, the influence of distance decreased while the impact of clutter and rainfall increased.

• Convergence Security Journal
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• v.20 no.2
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• pp.69-76
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• 2020

Blockchain 2.0 supports programmable smart contract for the various distributed application. However, the environment of running smart contract is limited in the blockchain, so the smart contract only get the deterministic information, such as block height, block hash, and so on. Therefore, some applications, which requires random information, such as lottery or batting, should use oracle service that supply the information outside of blockchain. This paper develops a random number generator oracle service. The random number generator oracle service use irregular big data as entropy source. This paper tests the randomness of bits sequence generated from oracle service using NIST SP800-22. This paper also describes the advantages of irregular big data in our model in perspective of cost comparing hardware entropy source.