• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.9-21
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• 2023

The recent conflict between Russia and Ukraine underscores the significant of military logistics support in modern warfare. Military logistics support is intricate and specialized, and traditionally centered on the mission-level operational analysis and functional models. Nevertheless, there is currently increasing demand for military logistics support even at the engagement level, especially for resupply using unmanned transport assets. In response to the demand, this study proposes a task model of the military logistics support for engagement-level analysis that relies on the logic of ammunition resupply below the battalion level. The model employs a decisions tree to establish the priority of resupply based on variables such as the enemy's level of threat and the remaining ammunition of the supported unit. The model's feasibility is demonstrated through a combat simulation using OneSAF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1478-1499
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• 2024

Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. COVID-19 become an active epidemic disease due to its spread around the globe. The main causes of the spread are through interaction and transmission of the droplets through coughing and sneezing. The spread can be minimized by isolating the susceptible patients. However, it necessitates remote monitoring to check the breathing issues of the patient remotely to minimize the interactions for spread minimization. Thus, in this article, we offer a wearable-IoTs-centered framework for remote monitoring and recognition of the breathing pattern and abnormal breath detection for timely providing the proper oxygen level required. We propose wearable sensors accelerometer and gyroscope-based breathing time-series data acquisition, temporal features extraction, and machine learning algorithms for pattern detection and abnormality identification. The sensors provide the data through Bluetooth and receive it at the server for further processing and recognition. We collect the six breathing patterns from the twenty subjects and each pattern is recorded for about five minutes. We match prediction accuracies of all machine learning models under study (i.e. Random forest, Gradient boosting tree, Decision tree, and K-nearest neighbor. Our results show that normal breathing and Bradypnea are the most correctly recognized breathing patterns. However, in some cases, algorithm recognizes kussmaul well also. Collectively, the classification outcomes of Random Forest and Gradient Boost Trees are better than the other two algorithms.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.927-940
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• 2017

Landslide has been a major disaster in Indonesia, and recent climate change and indiscriminate urban development around the mountains have increased landslide risks. Java Island, Indonesia, where more than half of Indonesia's population lives, is experiencing a great deal of damage due to frequent landslides. However, even in such a dangerous situation, the number of inhabitants residing in the landslide-prone area increases year by year, and it is necessary to develop a technique for analyzing landslide-hazardous and vulnerable areas. In this regard, this study aims to evaluate landslide susceptibility of Java, an island of Indonesia, by using GIS-based spatial prediction models. We constructed the geospatial database such as landslide locations, topography, hydrology, soil type, and land cover over the study area and created spatial prediction models by applying Weight of Evidence (WoE), decision trees algorithm and artificial neural network. The three models showed prediction accuracy of 66.95%, 67.04%, and 69.67%, respectively. The results of the study are expected to be useful for prevention of landslide damage for the future and landslide disaster management policies in Indonesia.

• Korean Journal of Radiology
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• v.24 no.1
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• pp.51-61
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• 2023

Objective: To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC. Materials and Methods: This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent ¹⁸F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models. Results: In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46-1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status. Conclusion: Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from ¹⁸F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.83-98
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• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.

• Journal of agriculture & life science
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• v.53 no.6
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• pp.23-33
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• 2019

In accordance with a change in the management information system containing the management record and planning for the entire national forest in South Korea by an amendment of the relevant law (The national forest management planning and methods, Korea Forest Service), in this study, average, the maximum, and the minimum values for DBH were presented while only average values were required before the amendment. In this regard, there is a need for an estimation algorithm by which all the existing values for DBH established before the revision can be converted to the highest and the lowest ones. The purpose of this study is to develop an estimation equation to automatically show the minimum and the maximum values for DBH for 12 main tree species from the data in the national forest management information system. In order to develop the estimation equation for the minimum and the maximum values for DBH, there was exploited the 6,858 fixed sample plots of the fifth and the sixth national forest inventory between in 2006 and 2015. Two estimation models were applied for DBH-tree age and DHB-tree height using such growth variables as DBH, tree age, and height, to draw the estimation equation for the maximum and the minimum values for DBH. The findings showed that the most suitable model to estimate the minimum and the maximum values for DBH was Dmin=a+bD+cH, Dmax=a+bD+cH with the variables of DBH and height. Based on these optimal models, the estimation equation was devised for the minimum and the maximum values for DBH for the 12 main tree species.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.42 no.3
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• pp.42-50
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• 2024

This study aims to develop a method for assessing the structural stability of large-old trees, which are typically evaluated primarily for their physiological health. To achieve this, finite element analysis(FEA) was applied to Zelkova serrata (Thunb.) Makino, which are significant as natural monuments, protected trees and street trees in urban areas and parks. Four representative large-old trees in Buyeo-gun, which had undergone surgical treatment, were selected for creating analytical models and being conducted FEA. The key findings are as follows: first, we made 3D models by acquiring 3D scanning and ultra sonic tomography data for analyzing FEA. We proposed methods for constructing 3D meshes and calculating self-weight and wind loads based on tree shape to interpret FEA. Second, the study confirmed that wind loads increase proportionally with the size of the tree and the shape of the canopy. Wind loads were observed irregularly in cases of unusual shapes or multiple segments of the canopy. Third, FEA's result of large-old trees with decay-induced cavities indicated that even under strong winds (70m/s) from typhoons, the likelihood of damage from trunk breakage due to self-weight was relatively low. Fourth, decay-induced cavities were found to affect internal stress distribution, leading to structural weaknesses. This highlights the need for measuring and documenting the shape of cavities during tree assessments. Accurate measurement and documentation of branching and tree form are also crucial, as complex branching and cavities increase the likelihood of structural weaknesses. The study also identified limitations in estimating self-weight, transmissivity, and projected areas of the canopy based on wind direction. To improve the accuracy of FEA, the method for enhancing precision of the 3D model and measuring the canopy and is necessary.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.5
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• pp.97-104
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• 2017

Greenspace enlargement through proper landscape planting is essential to creating a low carbon society. This study analyzed changes in stem diameter growth rates(DGR), ratios of below ground/above ground biomass(B/A), and carbon sequestration by age of major landscape tree species. Landscape trees for study were 11 species and 112 individuals planted in middle region of Korea. The DGR and B/A were analyzed based on data measured through a direct harvesting method including root digging. The carbon sequestration by tree age was estimated applying the derived regression models. The annual DGR at breast height of trees over 30 years averaged 0.72 cm/yr for deciduous species and 0.83 cm/yr for evergreen species. The B/A of the trees over 30 years averaged 0.23 for evergreen species and 0.40 for deciduous species, about 1.7 times higher than evergreen species. The B/A by age in this study did not correspond to the existing result that it decreased as tree ages became older. Of the study tree species, cumulative carbon sequestration over 25 years was greatest with Zelkova serrata(198.3 kg), followed by Prunus yedoensis(121.7 kg), Pinus koraiensis(117.5 kg), and Pinus densiflora (77.4 kg) in that order. The cumulative carbon sequestration by Z. serrata offset about 5% of carbon emissions per capita from household electricity use for the same period. The growth rates and carbon sequestration for landscape trees were much greater than those for forest trees even for the same species. Based on these results, landscape planting and management strategies were explored to improve carbon sequestration, including tree species selection, planting density, and growth ground improvement. This study breaks new ground in discovering changes in growth and carbon sequestration by age of landscape trees and is expected to be useful in establishing urban greenspaces towards a low carbon society.

• Journal of Preventive Medicine and Public Health
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• v.55 no.5
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• pp.415-423
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• 2022

Economic evaluations in the healthcare are used to assess economic efficiency of pharmaceuticals and medical interventions such as diagnoses and medical procedures. This study introduces the main concepts of economic evaluation across its key steps: planning, outcome and cost calculation, modeling, cost-effectiveness results, uncertainty analysis, and decision-making. When planning an economic evaluation, we determine the study population, intervention, comparators, perspectives, time horizon, discount rates, and type of economic evaluation. In healthcare economic evaluations, outcomes include changes in mortality, the survival rate, life years, and quality-adjusted life years, while costs include medical, non-medical, and productivity costs. Model-based economic evaluations, including decision tree and Markov models, are mainly used to calculate the total costs and total effects. In cost-effectiveness or costutility analyses, cost-effectiveness is evaluated using the incremental cost-effectiveness ratio, which is the additional cost per one additional unit of effectiveness gained by an intervention compared with a comparator. All outcomes have uncertainties owing to limited evidence, diverse methodologies, and unexplained variation. Thus, researchers should review these uncertainties and confirm their robustness. We hope to contribute to the establishment and dissemination of economic evaluation methodologies that reflect Korean clinical and research environment and ultimately improve the rationality of healthcare policies.