• The Journal of Korean Obstetrics and Gynecology
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• v.37 no.3
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• pp.63-84
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• 2024

Objectives: This study aims to understand the stress patterns and coping behaviors of women with infertility and to improve existing infertility stress assessment tools to develop a tool suited for Korean society. Methods: The study involved 10 women diagnosed with primary or secondary infertility. Data were collected through surveys and in-depth interviews. Participants were recruited voluntarily, and snowball sampling was used for additional recruitment. Data collection occurred from September 2023 to April 2024. Data analysis included Spearman's rank correlation, Mann-Whitney U test, and Kruskal-Wallis test. Interview results were analyzed using text mining and network analysis with Python 3.12. Results: There was a significant correlation between IVF/ICSI treatment and resilience scores, with non-IVF/ICSI groups showing higher resilience scores. Existing infertility stress assessment tools were generally useful but had limitations, such as discomfort with religious expressions and fixed gender roles, as well as issues with the number of items and response scales. Text mining of interview responses revealed key stress-related keywords including worry, depression, burden, pregnancy outcome, and health. Main stressors included uncertainty about pregnancy outcomes, physical discomfort during treatments, economic burdens, and emotional reactions from family and social relationships. Conclusions: This study identified the stress patterns of women with infertility through interviews. It showed the need for a new assessment tool to evaluate and support the complex stressors experienced by these women. Developing a comprehensive tool is essential for better understanding and managing the various stress factors faced by infertile women.

• Communications of Mathematical Education
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• v.30 no.3
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• pp.281-294
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• 2016

This study deals with the content that was developed by the authors and the utilization of the 'Interactive Engineering Math Laboratory (IEmath Lab).' IEmath Lab provides online review lectures as well as a wide range of examples and exercises from the curriculum of engineering mathematics courses. The lectures come with pre-coded Python-based SageMath cells through which students can run and modify the code directly from this free laboratory. IEmath Lab is accessible via mobile devices so that the students can use it anywhere, anytime for maximum learning effectiveness and achievement. IEmath Lab would be an ideal tool for the effective learning and teaching of engineering mathematics, which combines theory and practice.

• Journal of Advanced Navigation Technology
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• v.18 no.2
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• pp.191-199
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• 2014

Recently, researches to facilitate utilization by small and medium business (SMB) of cloud computing and big data paradigm, which is the booming adoption of IT area, has been on the increase. As one of these efforts, in this paper, we design and implement the prototype to tentatively build up Hadoop cluster under private cloud infrastructure environments. Prototype implementation are made on each hardware type such as single board, PC, and server and performance is measured. Also, we present the integrated verification results for the data analysis performance of the analysis software system running on top of realized prototypes by employing ASA (American Standard Association) Dataset. For this, we implement the analysis software system using several open sources such as R, Python, D3, and java and perform a test.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.194-202
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• 2019

Reliability analysis of the components frequently starts with the data that manufacturer provides. If enough failure data are collected from the field operations, the reliability should be recomputed and updated on the basis of the field failure data. However, when the failure time record for a component contains only a few observations, all statistical methodologies are limited. In this case, where the failure records for multiple number of identical components are available, a valid alternative is combining all the data from each component into one data set with enough sample size and utilizing the useful information in the censored data. The ROK Navy has been operating multiple Patrol Killer Guided missiles (PKGs) for several years. The Korea Multi-Function Control Console (KMFCC) is one of key components in PKG combat system. The maintenance record for the KMFCC contains less than ten failure observations and a censored datum. This paper proposes a Bayesian approach with a Dirichlet mixture model to estimate failure time density for KMFCC. Trends test for each component record indicated that null hypothesis, that failure occurrence is renewal process, is not rejected. Since the KMFCCs have been functioning under different operating environment, the failure time distribution may be a composition of a number of unknown distributions, i.e. a mixture distribution, rather than a single distribution. The Dirichlet mixture model was coded as probabilistic programming in Python using PyMC3. Then Markov Chain Monte Carlo (MCMC) sampling technique employed in PyMC3 probabilistically estimated the parameters' posterior distribution through the Dirichlet mixture model. The simulation results revealed that the mixture models provide superior fits to the combined data set over single models.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.115-118
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• 2002

소켓 프로그래밍(socket programming) 인터페이스를 지원하는 C/C++, perl, python 과 같은 언어들은 폴링(polling) 기능을 갖는 select() 함수를 제공한다. 이 select()함수를 이용할 경우, 단일 쓰레드(또는 프로세스)로 다중의 클라이언트 요청을 처리할 수 있다. 최근 네트워크 프로그래밍 분야에서 주목받는 자바 언어의 경우, 최신 JDK 1.4 의 비동기 입출력 패키지에서 select()함수를 제공하고 있으나, JDK 1.3을 포함한 그 이하의 버전에서는 아직까지 이 함수를 제공하지 않고 있다. 일반적으로 다중 쓰레드를 이용하여 소켓서버 응용프로그램을 개발할 경우, 코드가 단순해지고 응답이 빠른 장점이 있는 반면에 네트워크 연결이 증가할수록 다수의 쓰레드를 관리하는 일이 CPU에 큰 부담이 된다. 반면에 소켓폴링(socket polling)을 사용할 경우, 이러한 연결 유지에 대한 부담이 줄어드는 대신, 다중 쓰레드를 이용하는 방법에 비하여 구현이 어렵다. 본 논문에서는 다양한 시뮬레이션 환경에서 세가지 소켓 프로그래밍 모델에 대하여 그 성능을 비교평가 하였다. 이 세가지 모델은 단순 다중 쓰레드 모델(typical multi-thread model), 단일 쓰레드 소켓폴링 모델(socket polling with single-thread model), 다중 쓰레드 소켓폴링 모델(socket polling with multi-threadmodel)이다. 본 논문에서는 다중 쓰레드 소켓폴링 모델을 제안하고 JDK 1.3.1을 이용하여 구현하였다. 이 모델의 경우 복잡한 구조에도 불구하고 단순 다중 쓰레드 모델와 유사하거나 더 나은 성능을 보여주었다. 또한 동일한 용량의 쓰레드 풀(thread pool)을 사용하더라도 단순 다중 쓰레드 모델보다 더 많은 수의 클라이언트를 수용할 수 있는 장점이 있다. 이러한 결과를 바탕으로 본 연구팀에서 수행중인 MoIM-Messge서버의 네트워크 모듈로 다중 쓰레드 소켓폴링 모델을 적용하였다.

• Journal of Industrial Convergence
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• v.17 no.3
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• pp.23-29
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• 2019

Object recognition distinguish objects which are different from each other. But Face recognition distinguishes Identity of Faces with Similar Patterns. Feature extraction algorithm such as LBP, HOG, Gabor is being replaced with Deep Learning. As the technology that identify individual face with machine learning using Deep Learning Technology is developing, The Face Recognition Technology is being used in various field. In particular, the technology can provide individual and detailed service by being used in various offline environments requiring user identification, such as Smart Mirror. Face Recognition Technology can be developed as the technology that authenticate user easily by device like Smart Mirror and provide service authenticated user. In this paper, we present investigation about Face Recognition among various techniques for user authentication and analysis of Python source case of Face recognition and possibility of various service using Face Recognition Technology.

• Computers and Concrete
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• v.24 no.3
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• pp.207-222
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• 2019

This paper has presented an effective and accurate meso-scale finite element model for simulating the fracture process of concrete under compression-shear loading. In the proposed model, concrete is parted into four important phases: aggregates, cement matrix, interfacial transition zone (ITZ), and the initial defects. Aggregate particles were modelled as randomly distributed polygons with a varying size according to the sieve curve developed by Fuller and Thompson. With regard to initial defects, only voids are considered. Cohesive elements with zero thickness are inserted into the initial mesh of cement matrix and along the interface between aggregate and cement matrix to simulate the cracking process of concrete. The constitutive model provided by ABAQUS is modified based on Wang's experiment and used to describe the failure behaviour of cohesive elements. User defined programs for aggregate delivery, cohesive element insertion and modified facture constitutive model are developed based on Python language, and embedded into the commercial FEM package ABAQUS. The effectiveness and accuracy of the proposed model are firstly identified by comparing the numerical results with the experimental ones, and then it is used to investigate the effect of meso-structure on the macro behavior of concrete. The shear strength of concrete under different pressures is also involved in this study, which could provide a reference for the macroscopic simulation of concrete component under shear force.

• Journal of Aerospace System Engineering
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• v.16 no.5
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• pp.70-77
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• 2022

This paper proposes database generation and management system for small-pixelized airborne target recognition. The proposed system has five main features: 1) image extraction from in-flight test video frames, 2) automatic image archiving, 3) image data labeling and Meta data annotation, 4) virtual image data generation based on color channel convert conversion and seamless cloning and 5) HOG/LBP-based tiny-pixelized target augmented image data. The proposed framework is Python-based PyQt5 and has an interface that includes OpenCV. Using video files collected from flight tests, an image dataset for airborne target recognition on generates by using the proposed system and system input.

• The Journal of Korean Medicine
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• v.43 no.1
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• pp.73-86
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• 2022

Objectives: In the health care industry, the influence of online reviews is growing. As medical services are provided mainly by providers, those services have been managed by hospitals and clinics. However, direct promotions of medical services by providers are legally forbidden. Due to this reason, consumers, like patients and clients, search a lot of reviews on the Internet to get any information about hospitals, treatments, prices, etc. It can be determined that online reviews indicate the quality of hospitals, and that analysis should be done for sustainable hospital marketing. Method: Using a Python-based crawler, we collected reviews, written by real patients, who had experienced Korean medicine, about more than 14,000 reviews. To extract the most representative words, reviews were divided by positive and negative; after that reviews were pre-processed to get only nouns and adjectives to get TF(Term Frequency), DF(Document Frequency), and TF-IDF(Term Frequency - Inverse Document Frequency). Finally, to get some topics about reviews, aggregations of extracted words were analyzed by using LDA(Latent Dirichlet Allocation) methods. To avoid overlap, the number of topics is set by Davis visualization. Results and Conclusions: 6 and 3 topics extracted in each positive/negative review, analyzed by LDA Topic Model. The main factors, consisting of topics were 1) Response to patients and customers. 2) Customized treatment (consultation) and management. 3) Hospital/Clinic's environments.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.