• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2023년도 제67차 동계학술대회논문집 31권1호
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• pp.439-441
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• 2023

National park is one of the important types of protected area management systems established by IUCN and a management model for effective conservation and sustainable use of natural and cultural heritage in countries around the world, and it assumes important roles in conservation, scientific research, education, recreation and driving community development. This study takes Liaohe National Park in China, a typical representative of global coastal wetlands, as a case study, and uses python technology to collect travelogues and reviews of visitors from Mafengwo.com, Ctrip.com, Go.com, Meituan.com and Dianping.com as a source, and the text spans from 2015 to 2022. The results show that wildlife resources, natural landscape with river and sea, wetland ecology and fishing and hunting culture of northern China are fully reflected in the perceptions of visitors to Liaohe National Park. However, there is still much room for improvement in terms of supporting services and facilities, public education and tourists' experience and participation in Liaohe National Park. In this paper, we use python data visualization technology to study the public perception of wetland wildlife as the theme, and grasp the satisfaction, spatial distribution, activity content and emotional tendency of the public in the process of wetland wildlife as the theme, so as to better promote the Liaohe National Park to better carry out the public experience while strictly adhering to ecological protection, and to provide the Liaohe National Park with a better opportunity to This will provide scientific basis for the Liaohe National Park to play a better role in ecological civilization construction and education of ecological civilization awareness.

• 정보교육학회논문지
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• 제25권6호
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• pp.1015-1024
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• 2021

엔트리, Machine Learning for Kids, Teachable Machine과 같이 블록 기반 프로그래밍 언어에서 활용할 수 있도록 인공지능을 간단히 학습시킬 수 있는 다양한 플랫폼들이 존재한다. 그러나 이와 같은 플랫폼들은 별도의 메뉴를 통해 인공지능 학습을 진행한 다음, 학습된 모델을 코드 에디터에서 활용하는 방식을 따르고 있다. 이와 같은 방식은 학습되는 과정을 학생들이 더 직관적으로 살펴볼 수 있다는 장점이 있지만, 학습 메뉴와 코드 에디터를 모두 활용해야 한다는 단점도 존재한다. 본 논문에서는 코드 에디터에서 인공지능 학습과 코딩을 모두 진행할 수 있는 인공지능 블록을 개발한다. 본 인공지능 블록은 스크래치 블록으로 제시되지만 실제 학습 과정은 파이썬 서버를 통해 수행된다. 파란색 펜과 빨간색 펜을 분류하는 모델, 덴탈 마스크와 KF94 마스크를 분류하는 모델을 학습하는 과정을 통해 본 블록에 대해 상세히 기술한다. 또, 학습 성능 면에서 Teachable Machine와 큰 차이가 없음을 실험적으로 나타내었다.

• International journal of advanced smart convergence
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• 제10권1호
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• pp.56-61
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• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

• 정보교육학회논문지
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• 제23권2호
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• pp.179-188
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• 2019

본 연구는 초등학생의 논리성 향상을 위해 파이썬을 활용한 탐색 알고리즘 기반 수행시간 비교 및 분석 교육 프로그램을 개발하고 적용하여 그 효과를 분석했다. 본 교육 프로그램은 ${\bigcirc}{\bigcirc}$ 도내 초등학교 6학년 133명을 대상으로 실시한 사전 요구분석 결과를 활용하였고, ADDIE 모형의 절차에 따라 개발하였다. 개발한 교육 프로그램의 효과를 검증하기 위해서 ${\bigcirc}{\bigcirc}$대학교에서 실시한 교육기부 프로그램의 지원자 25명을 대상으로 6일간 42차시 수업을 진행하였고, GALT검사를 통해 교육의 사전 사후 효과를 비교 분석하였다. 분석해 본 결과, 본 연구에서 개발한 SW교육 프로그램이 초등학생의 논리성에 긍정적인 영향을 줄 수 있다는 것을 알 수 있었다.

• 실천공학교육논문지
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• 제15권2호
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• pp.419-427
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• 2023

In this study, optimal adhesion conditions to alleviate defects caused by heat shrinkage with FDM type 3D printers with machine learning are researched. Machine learning is one of the "statistical methods of extracting the law from data" and can be classified as supervised learning, unsupervised learning and reinforcement learning. Among them, a function model for adhesion between the bed and the output is presented using supervised learning specialized for optimization, which can be expected to reduce output defects with FDM type 3D printers by deriving conditions for optimum adhesion between the bed and the output. Machine learning codes prepared using Python generate a function model that predicts the effect of operating variables on adhesion using data obtained through adhesion testing. The adhesion prediction data and verification data have been shown to be very consistent, and the potential of this method is explained by conclusions.

• 반도체디스플레이기술학회지
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• 제18권3호
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• pp.25-30
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• 2019

In this paper, we have analyzed manufacturing problems of the scaffold with pores using FDM 3D printer and PLGA. We suggested the solutions using 3SC practical TRIZ. We selected the final solution used machine learning. We reduced number of experiments using most influential factor after analysis print factors. We printed the scaffold and measured pore size. We created the regression model using python tensorflow. The print condition data of measured pore size was used as training data. We predicted the pore size of printed condition using regression model. We printed the scaffold using the predicted the print condition data. We quantitatively compare the predicted scaffold pore size data and the measured scaffold pore size data. We got satisfactory result.

• 공학교육연구
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• 제26권6호
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• pp.71-78
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• 2023

ChatGPT significantly broadens the application of artificial intelligence (AI) services across various domains, with one of its primary functions being assistance in programming and coding. Nevertheless, due to the short history of ChatGPT, there have been few studies analyzing its coding capabilities in Korean higher education. In this paper, we evaluate it using exam questions from three foundational programming courses at S University. According to the experimental results, ChatGPT successfully generated Python, C, and JAVA programs, and the code quality is on par with that of high-achieving students. The powerful coding capabilities of ChatGPT imply the need for a strict prohibition of its usage in coding tests; however, it also suggests significant potential for enhancing practical exercises in the educational aspect.

• 실천공학교육논문지
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• 제16권3_spc호
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• pp.275-282
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• 2024

본 연구는 생성형 AI를 활용한 소프트웨어교육에 관한 수업모델을 연구하였다. 연구 목적은 ChatGPT를 소프트웨어교육에 활용함으로써 비전공학생들의 프로그래밍 수업에서 교수자의 보조자 역할로 ChatGPT를 활용하기 위함이다. 또한, ChatGPT를 이용해 학습자 개별 교육이 가능하도록 설계하고, 학생들이 필요로 한 시점에 즉각적인 피드백을 제공하고자 하였다. 연구 방법은 교양과목의 파이썬 수업을 듣는 컴퓨터 비전공자를 대상으로 ChatGPT를 보조자로 활용하여 수업을 진행하였다. 그리고 비전공학생의 프로그래밍 교육에서 ChatGPT가 보조자로서 가능성이 있는지 확인하였다. 학생들은 ChatGPT를 과제작성, 오류수정, 코딩 작성 및 지식 습득에 활발히 사용하였으며, 오류 해결에 많은 시간이 걸리는 것을 프로그램을 이해하는데 집중할 수 있는 등 다양한 이점을 확인하였다. ChatGPT가 학생들의 학습 효율을 높일 수 있는 가능성 볼 수 있었으며, 교육에 활용하는 데 있어서 더 많은 연구가 필요함을 알 수 있었다. 향후에는 ChatGPT를 활용한 교육 모델의 발전과 보완, 평가 방법에 관한 연구가 이루어질 것이다.

• 수산해양교육연구
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• 제28권2호
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• pp.376-383
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• 2016

In this paper, the blue-green algae monitoring program of applying IoT(Internet of things) technologies is designed and implemented that can check out the status of the river's water quality in real time. The proposed system is to extract the image data from the camera of raspberry pi by an wireless network, and it is analyzed through the HSV color model. We measure the temperature using a DS18B20 1-wire temperature sensor. The extracted information of image data and temperature is then analyzed in C and Python programs for use with Raspberry Pi. The XML data in PHP program is made from the analyzed information and provides Web services. It also allows to refer the XML data using mobile devices.

• Imaging Science in Dentistry
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• 제52권2호
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• pp.187-195
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• 2022

Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs(PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the "gold standard" for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.