• Journal of The Korean Association of Information Education
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• v.26 no.2
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• pp.121-128
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• 2022

In the 2022 revised curriculum general study released by the Ministry of Education in September 2021, environmental issues are emerging as a socially important topic, with climate and environmental education appearing at the forefront along with software education. In this study, by applying Python Monte Carlo simulation, a program for high school students was developed that combines environmental education and software education emphasized in the 2022 revised curriculum. The developed program verified the validity of the program with Lawshe's Content Validity Ratio for science, environment, and information subject education experts, and the verification results showed that the program meets the development purpose, environment, and information subject achievement standards.

• Journal of the Korea Convergence Society
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• v.11 no.1
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• pp.359-367
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• 2020

The purpose of this study is to explore text data of nursing start-up. 55 literatures were extracted from MEDLINE, Embase and Cochrane Library Data BASE. Text network analysis applied by using python network program. Key words with highest frequency and degree centrality were 'business', 'care', 'nursing', 'healthcare', 'service'. Keywords with highest degree centrality were 'mission', 'vision', 'team'. Based on the results nursing entrepreneurship support should be provided to develop competitive nursing services reflecting the specificity and science of nursing, to strengthen business competencies essential for nursing entrepreneurship, to expand nursing expertise and to present role models. The result will serve a basement to development systematic educational program and theory in nursing start-up.

• Structural Engineering and Mechanics
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• v.76 no.5
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• pp.653-661
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• 2020

The paper presents a study regarding rubber compressibility behavior. The objective is to analyze the effect of compression degree of rubber on its mechanical properties and propose a new methodology based on reverse engineering to predict compressibility degree based on uniaxial stretching test and Finite Element Analysis (FEA). In general, rubbers are considered to be almost incompressible and Poisson's ratio is close to 0.5. Since this property is intimately related to the rubber packing density, little changes in Poisson's ratio can lead to significant changes regarding mechanical behavior. The deviatory hyperelastic constants were obtained through experimental data fitting by least squares method for the most relevant constitutive models implemented in commercial software Abaqus, such as: Neo-Hooke, Mooney-Rivlin, Ogden, Yeoh and Arruda-Boyce, whereas the hydrostatic part was determined through an optimization algorithm implemented in the Abaqus environment by Python scripting. The simulation results presented great influence of the Poisson's ratio in the rubber specimen mechanical behavior mainly for high strain levels. A conventional pure volumetric compression test was also carried out in order to compare the results obtained by the proposed methodology.

• Journal of Fashion Business
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• v.27 no.6
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• pp.110-123
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• 2023

As social distancing strengthened after the COVID-19 incident, people looked for things they could do alone. Additionally, as people have more financial resources, they purchase products they had previously considered purchasing, and the phenomenon of giving gifts to oneself has also appeared. Accordingly, this study analyzed fashion product reviews of KakaoTalk Gift, the service to exchange gift via SNS mobile app, to discover the phenomenon of self-gifting and the differences from interpersonal-gifting. For post-hoc data, in collected 18,354 pieces after excluding unnecessary data using a Python-based web crawling technique. The self-gifting behavior of KakaoTalk Gift different from the previous study for self-gift. Regardless of the gift-giving contexts, it determines that most self-gift products are material items. There are differences in product types and price levels when choosing gifts for others and oneself. As a self-gift, people typically buy luxury jewelry and branded bags/wallets to wear and show off. As interpersonal, among fashion products, people usually buy beauty products that reflect less personal tastes. When gift-giving to others, people buy products to appropriate prices to reduce the burden on both. When gift-giving to oneself, people buy wanted products regardless of the price. This study is significant because it suggests a new direction in self-gift research by limited online places to give gifts.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.442-459
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• 2024

Currently, many industrial fields are pursuing research and development toward a hyper-connected society. However, as we become a hyper-connected society that perceives virtual reality as if it were reality, accurate classification of data to recognize objects, emotions and facial expressions must be accompanied. In other words, only when data meaning objects, emotions, and facial expressions are accurately classified will reliability of cognition and recognition be obtained not only in the physical world but also in a hyper-connected society. In addition, errors in perception and recognition of objects, emotions, and facial expressions can be reduced through big data analysis, and it will be protected from secondary incidents and damages. Therefore, in this study, we try to find out whether the classification of data is well done in the stage where AI with automatic cognition ability recognizes and recognizes objects, emotions, and facial expressions, and whether the data classified according to characteristics is a reliable classification result. In the experiment, when classifying data using a decision tree, we plan to conduct a study to find out whether the classification criteria of the data affect the classification criteria according to the degree of correlation between variables.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.162-164
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• 2019

본 논문에서는 물체인식 딥러닝 모델 생성에 필요한 라벨링(Labeling)과정에서 사용자가 다양한 기능을 활용하여 효과적인 학습 데이터를 구성할 수 있는 GUI 프로그램을 구현했다. 프로그램의 인터페이스는 파이썬 기반의 GUI 모듈인 Tkinter 를 활용하여, 실시간으로 이미지 데이터를 수집할 수 있는 크롤링(Crawling)기능과 미리 학습된 Retinanet 을 통해 이미지 데이터를 인식함으로써 자동으로 주석(Annotation) 과정을 수행할 수 있는 기능을 구성했다. 또한, 수집한 이미지 데이터를 다양한 효과와 노이즈, 변형 등으로 Augmentation 기능을 추가함으로써, 사용자가 모델을 학습하기 위한 데이터 전처리 단계를 하나의 GUI 프로그램에서 수행할 수 있도록 했다. 또한 사용자가 직접 학습한 모델을 추정 모델(Inference Model)로 변환하여 프로그램에 입력할 수 있도록 설계한다.

• Annual Conference of KIPS
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• 2016.10a
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• pp.316-318
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• 2016

최근 국내외에서 소프트웨어 교육의 중요성이 강조되고 있다. 2015년 개정 교육과정에서도 초 중등생들의 컴퓨팅사고력 계발에 관심이 집중되면서 초 중등학생들을 대상으로 코딩교육이 진행되고 있다. 컴퓨팅사고력 증진을 위해 그 핵심요소인 추상화와 자동화 능력을 신장시킬 여러 방법들이 연구되고 있다. 본 연구에서는 추상화 능력에 대해 교육심리 측면에서 학생들의 프로그래밍 능력 특성을 파악한 후, 컴퓨팅 사고력 계발에 도움을 주고자 한다. 이를 위해 일반계 고등학생들을 대상으로 파이썬 프로그래밍 교육을 보다 용이하게 진행하기 위하여 러플(RUR-PLE) 프로그래밍을 한 학기동안 학습한 후, 학생들의 추상적 사고수준이 프로그램을 이해하는데 어떻게 영향을 미치며 성별 간에 어떤 차이를 가지는지를 분석한다.

• Korean Journal of Computational Design and Engineering
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• v.10 no.5
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• pp.349-355
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• 2005

This paper concerns about how and why design frameworks for optimization should consider various software development environments such as MATLAB, VB, VBscript, Python, Tcl, PHP, Perl, and JAVA. The frameworks can be utilized by many engineers who have a basic concept about the optimization theory and/or basic knowledge about the computer programming languages. The framework will integrate a number of remote CAE tools, automatically execute them for design optimization, and have the capabilities of post-processing of data such as objective functions, state variables and design variables using a third-party spreadsheet program like Excel. The prototype framework developed in this study will be applied to various examples of optimization problems and show the validity of the proposed method of a framework implemenation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.9
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• pp.1210-1215
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• 2018

Recently, as the IoT(Internet of Things) and AI(Artificial Intelligence) technologies have developed, smart toys that can understand and act on the language of human beings are being studied. In this paper, we study voice learning using CNN(Convolutional Neural Network) by applying artificial intelligence based voice secretary technology to smart toy. When a human voice command gives, Smart Toy recognizes human voice, converts it into text, analyzes the morpheme, and conducts tagging and voice learning. As a result of test for the simulator program implemented using Python, no malfunction occurred in a single command. And satisfactory results were obtained within the selected simulation condition range.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.204-208
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• 2018

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.