• ETRI Journal
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• v.39 no.5
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• pp.662-671
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• 2017

Coronary artery disease (CAD) is a major cause of death in the world. As a non-invasive imaging modality, computed tomography angiography (CTA) is now usually used in clinical practice for CAD diagnosis. Precise quantification of coronary stenosis is of great interest for diagnosis and treatment planning. In this paper, a novel cluster method based on a Modified Student's t-Mixture Model is applied to separate the region of vessel lumen from other tissues. Then, the area of the vessel lumen in each slice is computed and the estimated value of it is fitted with a curve. Finally, the location and the level of the most stenoses are captured by comparing the calculated and fitted areas of the vessel. The proposed method has been applied to 17 clinical CTA datasets and the results have been compared with reference standard degrees of stenosis defined by an expert. The results of the experiment indicate that the proposed method can accurately quantify the stenosis of the coronary artery in CTA.

• Smart Media Journal
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• v.7 no.2
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• pp.23-33
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• 2018

A musical sheet is read by optical music recognition (OMR) systems that automatically recognize and reconstruct the read data to convert them into a machine-readable format such as XML so that the music can be played. This process, however, is very challenging due to the large variety of musical styles, symbol notation, and other distortions. In this paper, we present a model for the recognition of musical symbols through the use of a mobile application, whereby a camera is used to capture the input image; therefore, additional difficulties arise due to variations of the illumination and distortions. For our proposed model, we first generate a line adjacency graph (LAG) to remove the staff lines and to perform primitive detection. After symbol segmentation using the primitive information, we use a covariance-matching method to estimate the similarity between every symbol and pre-defined templates. This method generates the three hypotheses with the highest scores for likelihood measurement. We also add a global consistency (time measurements) to verify the three hypotheses in accordance with the structure of the musical sheets; one of the three hypotheses is chosen through a final decision. The results of the experiment show that our proposed method leads to promising results.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.135-142
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• 2019

Given the ability and skills required by modern people, marketing can be divided into knowledge-related skill such as marketing plans, market segmentation, and marketing mix management and supportive skill such as communication, inter-organizational management, creativity, and decision making. Knowledge related skills can be nurtured in existing marketing classes, but it is recognized that special educational programs such as self marketing are needed to develop and train supportive skills regardless of education levels or major education. This paper is aimed to design for marketing educational program for the self marketing. In this study, a DACUM method job analysis to extract contents by specialists such as model setting of task and job, job statement, job analysis, education course development, and so on. In the first place, this report presents job analysis model by procedures for developing selection criteria of examination questions of the self marketing qualification. The first step is preparation for job analysis, the second step: the establishment of job models, the third step : the job specification and task analysis, the fourth step: the review of job model, the fifth step: the establishment of subjects for examination matrix table for making questions.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.85-90
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• 2021

In this paper, fall detection using individual wearable devices for older people is considered. To design a low-cost wearable device for reliable fall detection, we present a comprehensive analysis of two representative models. One is a machine learning model composed of a decision tree, random forest, and Support Vector Machine(SVM). The other is a deep learning model relying on a one-dimensional(1D) Convolutional Neural Network(CNN). By considering data segmentation, preprocessing, and feature extraction methods applied to the input data, we also evaluate the considered models' validity. Simulation results verify the efficacy of the deep learning model showing improved overall performance.

• Korean Journal of Artificial Intelligence
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• v.11 no.3
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• pp.17-22
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• 2023

In this paper, we propose a black ice detection system using Convolutional Neural Networks (CNNs). Black ice poses a serious threat to road safety, particularly during winter conditions. To overcome this problem, we introduce a CNN-based architecture for real-time black ice detection with an encoder-decoder network, specifically designed for real-time black ice detection using thermal images. To train the network, we establish a specialized experimental platform to capture thermal images of various black ice formations on diverse road surfaces, including cement and asphalt. This enables us to curate a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Additionally, in order to enhance the accuracy of black ice detection, we propose a multi-scale dilation convolution feature fusion (MsDC-FF) technique. This proposed technique dynamically adjusts the dilation ratios based on the input image's resolution, improving the network's ability to capture fine-grained details. Experimental results demonstrate the superior performance of our proposed network model compared to conventional image segmentation models. Our model achieved an mIoU of 95.93%, while LinkNet achieved an mIoU of 95.39%. Therefore, it is concluded that the proposed model in this paper could offer a promising solution for real-time black ice detection, thereby enhancing road safety during winter conditions.

• Annual Conference of KIPS
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• 2021.05a
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• pp.454-456
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• 2021

본 논문은 웹의 발달로 인하여 의료 서비스들이 기존의 Client-Server 방식의 제품에서 Web 방식의 제품으로 변경되고 있는 현대 흐름에서 인공지능 어플리케이션 또한 Web 으로 서비스 하기 위한 방법과 구현된 요로결석 AI 어플리케이션에 대해 기술한다. 이를 구현하기 위해 Python 기반의 Flask 라는 마이크로 웹 프레임워크를 사용하여 DICOM 핸들링, Pre-Processing, Mask 를 생성하고 Predict 결과를 Model Serving 을 통하여 Urinary Stone Segmentation Model 이 서비스되는 인공지능 웹 어플리케이션 동작 방식과 수행 결과를 보인다.

• Journal of the Korean Society of Safety
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• v.39 no.1
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• pp.96-103
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• 2024

The demand for mass calculation of offsite consequence analysis to conduct exhaustive single-unit or multi-unit Level 3 PSA is increasing. In order to perform efficient offsite consequence analyses, the Korea Atomic Energy Research Institute is conducting model optimization studies to minimize the analysis time while maintaining the accuracy of the results. A previous study developed a model optimization method using efficient plume segmentation and verified its effectiveness. In this study, we investigated the possibility of optimizing the model through particle size distribution setting by checking the reduction in analysis time and deviation of the results. Our findings indicate that particle size distribution setting affects the results, but its effect on analysis time is insignificant. Therefore, it is advantageous to set the particle size distribution as fine as possible. Furthermore, we evaluated the effect of multithreading and confirmed its efficiency. Future optimization studies should be conducted on various input factors of offsite consequence analysis, such as spatial grid settings.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.1975-1988
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• 2024

It is important to maintain cladding integrity in spent nuclear fuel management. This study proposes a numerical analysis method to evaluate the fracture resistance of irradiated zirconium alloy cladding under pinch load known to cause Mode-III failure. The mechanical behavior and fracture of the cladding under pinch loading can be evaluated by a Ring Compression Test (RCT). To simulate the fracture of hydride precipitates, zirconium matrix, and Zr/hydride interfaces under the stress field generated by RCT, a micro-structure crack propagation simulation method based on Continuum Damage Mechanics (CDM) has been proposed. Our RCT simulation model was constructed from microscopic images of irradiated cladding. In this study, we developed an automated process to generate a pixel-based finite element model by separating the hydride precipitates, zirconium matrix, and interfaces using an image segmentation method. The appropriate element size was selected to ensure the efficiency and accuracy of a crack propagation simulation. The load-displacement curves and strain energies from RCT were compared and analyzed with the simulation results of different element sizes. The finalized RCT simulation model can be used to establish the failure criterion of fuel rods under pinch loading. The advantages and limitations of the proposed method are fully discussed here.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.1
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• pp.31-38
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• 2005

Importance of road that is country base equipment is occupying fair part. Therefore, establishment of road and maintenance expense for road management are increasing continuously. These problem can manage efficiently through data model construction that take advantage of framework data. But, because of difference of method of study in research institution, framework data research was constructed being overlapped until current. This is because framework data research was no access of application side. Therefore, National Geographic Information Institute presented subject framework data model guide through framework data model standardization business. This research constructed road management data model that take advantage of traffic framework data. Therefore, we can check equal data construction and reduce expense accordingly. Also, because there are not data model development instances by framework data model, it is difficult that judge whether is suitable to apply framework data model guide. Hence, in this study, the extended road management data medel and the suitability of framework data is presented.

• The Journal of Korea Robotics Society
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• v.8 no.1
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• pp.1-7
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• 2013

Body gesture Recognition has been one of the interested research field for Human-Robot Interaction(HRI). Most of the conventional body gesture recognition algorithms used Hidden Markov Model(HMM) for modeling gestures which have spatio-temporal variabilities. However, HMM-based algorithms have difficulties excluding meaningless gestures. Besides, it is necessary for conventional body gesture recognition algorithms to perform gesture segmentation first, then sends the extracted gesture to the HMM for gesture recognition. This separated system causes time delay between two continuing gestures to be recognized, and it makes the system inappropriate for continuous gesture recognition. To overcome these two limitations, this paper suggests primitive body model encoding, which performs spatio/temporal quantization of motions from human body model and encodes them into predefined primitive codes for each link of a body model, and Selective/Asynchronous Input-Parallel State machine(SAI-PSM) for multiple-simultaneous gesture recognition. The experimental results showed that the proposed gesture recognition system using primitive body model encoding and SAI-PSM can exclude meaningless gestures well from the continuous body model data, while performing multiple-simultaneous gesture recognition without losing recognition rates compared to the previous HMM-based work.