• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.366-369
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• 2003

본 연구에서는 강건한 문맥의존 음향모델을 작성하기 위한 기초적인 연구로서 문맥환경과 상태수의 변화에 따른 음향모델의 성능을 고찰하고자 한다. 음성은 시간함수로 표현되며 음절, 단어, 연속음성을 발성할때 자음과 모음에 따라 발성시간에 차이가 있으며 음성인식의 최소 인식단위로 널리 사용되는 음소의 앞과 뒤에 오는 문맥환경에 따라 인식성능에 많은 차이를 보이고 있다. 따라서 본 연구에서는 시간의 변화(상태수의 변화)와 상태분할 과정에서 문맥환경의 변화를 고려하여 다양한 형태의 문맥의존 음향모델을 작성하였다. 모델학습은 음소결정트리 기반 SSS 알고리즘(Phonetic Decision Tree-based Successive State Splitting： PDT-555)을 이용하였다 PDT-SSS 알고리즘은 미지의 문맥정보를 해결하기 위해 문맥방향과 시간방향으로 목표 상태수에 도달할 때까지 상태분할을 수행하여 모델을 작성하는 방법이다. 본 연구에서 강건한 문맥의존 음향모델을 학습하기 위한 방법의 유효성을 확인하기 위해 국어공학센터의 452 단어를 대상으로 음소와 단어인식 실험을 수행하였다. 실험결과, 음성의 시간변이에 따른 모델의 상태수와 각 음소의 문맥환경에 따라 인식성능의 변화를 고찰할 수 있었다. 따라서 본 연구는 향후 음성인식 시스템의 강건한 문맥의존 음향모델을 작성하는데 유효할 것으로 기대된다.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.435-446
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• 2022

Identifying ship types is an important process to prevent illegal activities on territorial waters and assess marine traffic of Vessel Traffic Services Officer (VTSO). However, the Terrestrial Automatic Identification System (T-AIS) collected at the ground station has over 50% of vessels that do not contain the ship type information. Therefore, this study proposes a method of identifying ship types through the Random Forest Classifier (RFC) from dynamic and static data of AIS and V-Pass for one year and the Ulsan waters. With the hypothesis that six features, the speed, course, length, breadth, time, and location, enable to estimate of the ship type, four classification models were generated depending on length or breadth information since 81.9% of ships fully contain the two information. The accuracy were average 96.4% and 77.4% in the presence and absence of size information. The result shows that the proposed method is adaptable to identifying ship types.

• ETRI Journal
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• v.45 no.5
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• pp.746-757
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• 2023

This study presents a novel safe landing algorithm for urban drone deliveries. The rapid advancement of drone technology has given rise to various delivery services for everyday necessities and emergency relief efforts. However, the reliability of drone delivery technology is still insufficient for application in urban environments. The proposed approach uses the "landing angle control" method to allow the drone to land vertically and a rapidly exploring random tree-based collision avoidance algorithm to generate safe and efficient vertical landing paths for drones while avoiding common urban obstacles like trees, street lights, utility poles, and wires; these methods allow for precise and reliable urban drone delivery. We verified the approach within a Gazebo simulation operated through ROS using a six-degree-of-freedom drone model and sensors with similar specifications to actual models. The performance of the algorithms was tested in various scenarios by comparing it with that of stateof-the-art 3D path planning algorithms.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.91-99
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• 2024

In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.

• The Journal of Korean Medicine
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• v.35 no.3
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• pp.93-102
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• 2014

Objectives: This study aimed at revising the Korean Out-patient Groups for Korean Medicine (KOPG-OM, version 1.0) based on clinical similarity and resource use, by using the accumulated claims data, and evaluating the validity of the revised classification system. Methods: A clinical specialist panel involving 19 specialists from 8 Korean medicine (KM) specialty areas reviewed the classification tree, diagnosis groups and procedure groups in terms of clinical similarity. Several models of outpatient grouping were formulated, with the validity of each tested based on the $R^2$ coefficient of determination for the treatment costs of all visits. To add age splits, the variances of treatment costs by age groups were also analyzed. These statistical analyses were performed using KM claims data of National Health Insurance from 2010 to 2012. Results: The classification tree designed via panel discussions was used to allocate outpatient cases to 26 diagnosis groups, with cases involving procedures such as acupuncture, moxibustion and cupping, then allocated to 9 procedure groups in each diagnosis group. The cases without procedures were categorized into the visit index - medication group. This process resulted in 298 outpatient groups. The $R^2$ values for treatment costs of all visits ranged from 0.38 to 0.69 depending on the providers' types. Conclusions: The revised model of KOPG-KM has a higher validity for outpatient classification than the current system and can provide better management of the costs of outpatient care in KM.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.668-677
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• 2012

This study analyzed the related factors affecting to perceived gastritis or perceived enteritis for 11,753 Korean high school seniors who participated in the 2009 Korea Youth Risk Behavior Web-based Survey (KYHRBWS). Of the subjects, 5,685 (47.6%)were male and 6,068(52.4%) were female and 8.7% of the students responded that they had suffered from gastritis or enteritis for a long time and the females had a slightly higher attack rate of gastritis or enteritis. Survey logistic regression models and decision tree analysis were used to calculate odd ratios and 95% confidence intervals. As a result, there was affecting to their stress and health behaviors in the risk of gastritis and enteritis, and that their lower level perceived health, smoking, heavy drinking or starting drinking before they were 13 years old and a higher level of perceived stress significantly affected the risk of gastritis or enteritis in the subjects(p<.001).

• The Journal of Society for e-Business Studies
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• v.12 no.4
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• pp.145-163
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• 2007

Service-Oriented Architecture is a style of information systems that enables the creation of applications that are built by combining loosely coupled and interoperable services. A service is an implementation of business functionality with proper granularity and invoked with well-defined interface. In service modeling, when the granularity of a service is finer, the reusability and flexibility of the service is lower. For solving this problem concerns with the service granularity, it is critical to identify and define coarse-grained services from the domain analysis model. In this paper, we define the process to identify services from the Use Case model elicited from domain analysis. A task tree is derived from Use Cases and their descriptions, and Use Cases are reconstructed by the composition and decomposition of the task tree. Reconstructed Use Cases are defined and specified as services. Because our method is based on the widely used UML Use Case models, it can be helpful to minimize time and cost for developing services in various platforms and domains.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.1
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• pp.11-18
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• 2022

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.100-103
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• 2021

Most companies study why employees resign their jobs to prevent the outflow of excellent human resources. To obtain the data needed for the study, employees are interviewed or surveyed before resignation. However, it is difficult to get accurate results because employees do not want to express their opinions that may be disadvantageous to working in a survey. Meanwhile, according to the data released by the Korea Labor Institute, the greater the difference between the minimum level of education required by companies and the level of employees' academic background, the greater the tendency to resign jobs. Therefore, based on these data, in this study, we would like to predict whether employees will leave the company based on data such as major, education level and company type. We generate four kinds of resignation prediction models using Decision Tree, XGBoost, kNN and SVM, and compared their respective performance. As a result, we could identify various factors that were not covered in previous study. It is expected that the resignation prediction model help companies recognize employees who intend to leave the company in advance.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.10
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• pp.761-765
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• 2009

This research aims an optimal placement of Mobile Base Station (MBS) under HAP based network configurations with the restrictions of HAP capabilities. With clustering algorithm based on BIRCH, mobile ground nodes are clustered and the centroid of the clusters will be the location of MBS. The hierarchical structure of BIRCH enables mobile node management by CF tree and the restrictions of maximum nodes per MBS and maximum radio coverage are accomplished by splitting and merging clusters. Mobility models based on Jeju island are used for simulations and such restrictions are met with proper placement of MBS.