• Journal of Korean Society of Forest Science
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• v.106 no.3
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• pp.320-329
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• 2017

This study aims to evaluate whether the accuracy of LiDAR DEM is affected by the changes of the five input levels ('1','3','5','7' and '9') of median parameter ($F_{md}$), mean parameter ($F_{mn}$) of the Filtering Algorithm (FA) in the GroundFilter module and median parameter ($I_{md}$), mean parameter ($I_{mn}$) of the Interpolation Algorithm (IA) in the GridSurfaceCreate module of the FUSION in order to present the combination of parameter levels producing the most accurate LiDAR DEM. The accuracy is measured by the residuals calculated by difference between the field elevation values and their corresponding DEM elevation values. A multi-way ANOVA is used to statistically examine whether there are effects of parameter level changes on the means of the residuals. The Tukey HSD is conducted as a post-hoc test. The results of the multi- way ANOVA test show that the changes in the levels of $F_{md}$, $F_{mn}$, $I_{mn}$ have significant effects on the DEM accuracy with the significant interaction effect between $F_{md}$ and $F_{mn}$. Therefore, the level of $F_{md}$, $F_{mn}$, and the interaction between two variables are considered to be factors affecting the accuracy of LiDAR DEM as well as the level of $I_{mn}$. As the results of the Tukey HSD test on the combination levels of $F_{md}{\ast}F_{mn}$, the mean of residuals of the '$9{\ast}3$' combination provides the highest accuracy while the '$1{\ast}1$' combination provides the lowest one. Regarding $I_{mn}$ levels, the mean of residuals of the both '3' and '1' provides the highest accuracy. This study can contribute to improve the accuracy of the forest attributes as well as the topographic information extracted from the LiDAR data.

• The KIPS Transactions:PartA
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• v.17A no.3
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• pp.159-166
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• 2010

In this paper, to obtain the Minimum Spanning Tree (MST) from the graph with several nodes having the same weight, I applied both Bor$\dot{u}$vka and Kruskal MST algorithms. The result came out to such a way that Kruskal MST algorithm succeeded to obtain MST, but not did the Prim MST algorithm. It is also found that an algorithm that chooses Inter-MSF MWE in the $2^{nd}$ stage of Bor$\dot{u}$vka is quite complicating. The $1^{st}$ stage of Bor$\dot{u}$vka has an advantage of obtaining Minimum Spanning Forest (MSF) with the least number of the edges, and on the other hand, Kruskal MST algorithm has an advantage of always obtaining MST though it deals with all the edges. Therefore, this paper suggests an Hybrid MST algorithm which consists of the merits of both Bor$\dot{u}$vka's $1^{st}$ stage and Kruskal MST algorithm. When applied additionally to 6 graphs, Hybrid MST algorithm has a same effect as that of Kruskal MST algorithm. Also, comparing the algorithm performance speed and capacity, Hybrid MST algorithm has shown the greatest performance Therefore, the suggested algorithm can be used as the generalized MST algorithm.

• Journal of the Korean Society of Visualization
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• v.19 no.3
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• pp.31-38
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• 2021

The energy harvesting system using an inverted flag is analyzed by using an immersed boundary method to consider the fluid and solid interaction. The inverted flag flutters at a lower critical velocity than a conventional flag. A fluttering motion is classified into straight, symmetric, asymmetric, biased, and over flapping modes. The optimal energy harvesting efficiency is observed at the biased flapping mode. Using the three different machine learning algorithms, i.e., artificial neural network, random forest, support vector regression, the energy harvesting efficiency is predicted by taking bending rigidity, inclination angle, and flapping frequency as input variables. The R² value of the artificial neural network and random forest algorithms is observed to be more than 0.9.

• Journal of the Korea Society for Simulation
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• v.22 no.4
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• pp.139-147
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• 2013

This paper proposes a novel method for feature selection for mass spectrometric proteomic data based on Random Forest. The method includes an effective preprocessing step to filter a large amount of redundant features with high correlation and applies a tournament strategy to get an optimal feature subset. Experiments on three public datasets, Ovarian 4-3-02, Ovarian 7-8-02 and Prostate shows that the new method achieves high performance comparing with widely used methods and balanced rate of specificity and sensitivity.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.154-161
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• 2018

As the number of smartphone users increases, research on indoor location recognition service is necessary. Access to indoor locations is predominantly WiFi, Bluetooth, etc., but in most quarters, WiFi is equipped with WiFi functionality, which uses WiFi features to provide WiFi functionality. The study uses the random forest algorithm, which employs the fingerprint index of the acquired WiFi and the use of the multI-value classification method, which employs the receiver signal strength of the acquired WiFi. As the data of the fingerprint, a total of 4 radio maps using the Mac address together with the received signal strength were used. The experiment was conducted in a limited indoor space and compared to an indoor location recognition system using an existing random forest, similar to the method proposed in this study for experimental analysis. Experiments have shown that the system's positioning accuracy as suggested by this study is approximately 5.8 % higher than that of a conventional indoor location recognition system using a random forest, and that its location recognition speed is consistent and faster than that of a study.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.39-46
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• 2022

As the link between traffic accidents and social and economic losses has been confirmed, there is a growing interest in developing safety policies based on crash data and a need for countermeasures to reduce severe crash outcomes such as severe injuries and fatalities. In this study, we select Daejeon city where the relative proportion of fatal crashes is high, as a case study region and focus on the severity of pedestrian crashes. After a series of data manipulation process, we run machine learning algorithms for the optimal model selection and variable identification. Of nine algorithms applied, AdaBoost and Random Forest (ensemble based ones) outperform others in terms of performance metrics. Based on the results, we identify major influential factors (i.e., the age of pedestrian as 70s or 20s, pedestrian crossing) on pedestrian crashes in Daejeon, and suggest them as measures for reducing severe outcomes.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.1
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• pp.20-27
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• 2011

High-definition, geo-referenced digital climate maps can be produced by applying watershed-specific modules to adjust synoptic observations for local effects including cold air drainage. Since there is no information available on North Korean watersheds, existing geospatial technology for digital climate mapping cannot be transferred to North Korea. We applied a watershed extraction algorithm based on ArcHydro to the North Korean portion of ASTER GDEM and utilized geographical information on major rivers and mountains to adjust the products. Proposed hydrologic zoning system for North Korean watersheds consists of 21 river basins, 93 stream basins and 885 catchments. Combined with the existing 840 South Korean hydrologic units, we now have a complete set of 1,725 catchments which may serve a framework for digital climate modeling across whole land area of the Korean Peninsula.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.172-174
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• 2021

In the existing industry, maintenance was carried out in the form of preventive maintenance such as occurrence of unnecessary idle time due to limited monitoring and maintenance. However, with the advent of the Fourth Industrial Revolution, real-time monitoring is possible in many industries including mining, manufacturing, oil and gas, and commercial agriculture, and it is desired to minimize idle time due to maintenance. In particular, there is a growing interest in predictive maintenance that can reduce costs and maximize operational efficiency by predicting and maintaining a failure before equipment and equipment fail. In this study, we look at the predictive maintenance technology that can verify the abnormal condition of the equipment of the smart factory in advance and monitor the abnormal condition in real time.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.173-178
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• 2023

As Korean literature spreads around the world, its position in the overseas publishing market has become important. As demand in the overseas publishing market continues to grow, it is essential to predict future book sales and analyze the characteristics of books that have been highly favored by overseas readers in the past. In this study, we proposed ensemble learning based prediction model and analyzed characteristics of the cumulative sales of more than 5,000 copies classified as good sellers published overseas over the past 5 years. We applied the five ensemble learning models, i.e., XGBoost, Gradient Boosting, Adaboost, LightGBM, and Random Forest, and compared them with other machine learning algorithms, i.e., Support Vector Machine, Logistic Regression, and Deep Learning. Our experimental results showed that the ensemble algorithm outperforms other approaches in troubleshooting imbalanced data. In particular, the LightGBM model obtained an AUC value of 99.86% which is the best prediction performance. Among the features used for prediction, the most important feature is the author's number of overseas publications, and the second important feature is publication in countries with the largest publication market size. The number of evaluation participants is also an important feature. In addition, text mining was performed on the four book reviews that sold the most among good-selling books. Many reviews were interested in stories, characters, and writers and it seems that support for translation is needed as many of the keywords of "translation" appear in low-rated reviews.