• 수산해양교육연구
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• 제20권2호
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• pp.184-200
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• 2008

The purpose of this paper is to develop instructional activity support model for capstone design in order for improving creative engineering education. To do this, having extracted the core idea of capstone design, and elicited core learning activity process, and grasped core supportive factors according to each core learning activity process that elicited, an improved instructional design model for capstone design was then developed through formative evaluation with respect to the draft of the instructional system development model for capstone design. As to major research methods, case analysis, requirements analysis through interview, and formative evaluation by experts were employed, and then research studies were undertaken. The formative evaluation by experts was carried out for two hours in 2007, and the experts participated in the evaluation consisted of total 6 persons: two specialists of capstone design contents, two professionals in field works, and two expert instructional designers in education engineering. Interview results had been reflected in this research when developing final instructional design model for capstone design. The core learning activity process of the final instructional design model for caption design, which developed in this research, comprises following stages: (1) Team building $\rightarrow$ (2) Integrated meeting between industry and academy $\rightarrow$ (3) Analysis of tasks $\rightarrow$ (4) Clarification of tasks $\rightarrow$(5) Seeking solutions for issues $\rightarrow$ (6) Eliciting priority of solutions $\rightarrow$ (7) Designing solutions and construction $\rightarrow$ (8) Exhibiting outcomes and presentation $\rightarrow$(9) Gaining comprehensive insights Also, in the core learning activity process, supportive factors that support implementation of each step were presented having been categorized into facilitator (teacher, and professionals in field works), learner and tool, etc.

• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.190-198
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• 2023

Sentiment analysis can be applied to all texts generated from websites, blogs, messengers, etc. The study fulfills an artificial intelligence sentiment analysis estimating visiting evaluation opinions (reviews) and visitor ratings, and suggests a deep learning model which foretells either an affirmative or a negative inclination for new reviews. This study operates review big data about Jeju tourist attractions which are extracted from Google from October 1st, 2021 to November 30th, 2021. The normalization data used in the propensity prediction modeling of this study were divided into training data and test data at a 7.5:2.5 ratio, and the CNN classification neural network was used for learning. The predictive model of the research indicates an accuracy of approximately 84.72%, which shows that it can upgrade performance in the future as evaluating its error rate and learning precision.

• 디지털산업정보학회논문지
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• 제19권1호
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• pp.91-107
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• 2023

With the recent expansion of the commerce ecosystem, a large number of user evaluations have been produced. Accordingly, attempts to create business insights using user evaluation data have been actively made. However, since user evaluation can change after the user experiences the product, it is difficult to say that the analysis based only on reviews immediately after purchase fully reflects the user's evaluation of the product. Moreover, studies conducted so far on user evaluation have overlooked the fact that the length of time a user has used a product can affect the user's product evaluation. Therefore, in this study, we build a model that predicts the direction of change in the user's rating after use from the user's rating and reviews immediately after purchase. In particular, the proposed model reflects the product's period of use in predicting the change direction of the star rating. However, since the posterior information on the duration of product use cannot be used as input in the inference process, we propose a structure that utilizes information about the product's period of use using an auxiliary classifier. As a result of an experiment using 599,889 user evaluation data collected from the shopping platform 'N' company, we confirmed that the proposed model performed better than the existing model in terms of accuracy.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.72-74
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• 2021

다양한 분야에서 현재 활용되고 있는 딥러닝 과정은 데이터 준비, 데이터 전처리, 모델 생성, 모델 학습, 모델 평가로 구성 된다. 이중 모델 학습 과정에서 손실함수는 모델이 학습하면서 출력한 값을 실제 값과 비교하여 그 차이를 출력하게 되고, 출력된 손실값을 기반으로 모델은 역전파 알고리즘을 통해 손실값이 감소하는 방향으로 가중치를 수정해가며 학습을 진행한다. 본 논문에서는 바이오마커 추출을 위한 딥러닝 모델에서 사용될 신경망 출력 값의 손실도를 측정하여 출력해주는 다양한 손실함수를 분석하고 실험을 통해 최적의 손실함수를 찾아내고자 한다.

• Journal of Platform Technology
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• 제11권4호
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• pp.80-88
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• 2023

인공지능 분야에서 학습모델을 구성하기 위해서는 학습데이터의 수집이 선행되어야 하며, 학습데이터를 학습모델 구성이 이루어지는 중앙 서버로 전달하여야 한다. 연합 학습은 클라이언트 측면의 데이터 이동없이 협력적은 방법으로 전역 학습 모델을 구성하는 기계학습 방법이다. 연합학습은 개인 정보를 보호하기 위해 활용될 수 있으며, 개별 클라이언트에서 로컬 학습모델을 구성한 후 로컬 모델의 매개변수를 중앙에서 집계하여 전역 모델을 업데이트한다. 이 본문에서는 연합학습의 개선을 위해 기존의 학습 결과인 학습 매개변수를 사용한다. 이를 위해 연합학습 프레임워크인 Flower를 사용하여 실험을 수행한 후 알고리즘의 수행시간 및 최적화에 따른 결과를 평가하여 제시한다.

• 대한지구과학교육학회지
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• 제10권2호
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• pp.91-103
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• 2017

이 연구는 거꾸로 수업에 기반 한 지구과학수업 모델을 개발하고, 초등 예비 교사의 과학수업에 수업모델을 적용하여 그 효과를 분석하였다. 본 연구에서 개발한 수업모델은 교실수업에서 이루어지는 모든 내용과 정보를 사전에 학생들에게 제공하는 것으로 거꾸로 수업에서의 선행과제를 대체하였다. 교실수업에서 '지식'내용은 교수에 의한 강의식 설명으로 20분 동안 학생들에게 수행되었으며, 그 후 학생들은 지식과 관련된 심화탐구문제를 토론 협력을 통해 모둠별로 해결하였다. 탐구문제 해결 후, 모둠원 중 발표자로 선정된 한 학생이 모든 학생들 앞에서 모둠에서 수행한 결과를 발표하였다. 14차시 수업 중에 나타난 학생들의 학습과정과 사전에 제공된 학습 자료의 포트폴리오는 기말고사를 대처한 한 학기의 과정평가로 교수에 의해 운영되었다. 수업에 참여한 많은 학생들은 본 수업 모델이 수업에 대한 집중력, 그리고 토론기술과 사고능력을 향상시키는데 있어서 매우 긍정적으로 작용한다는 인식을 나타냈다. 결론 적으로 본 수업모델은 지구과학수업에서 적용하는데 매우 유용하다고 볼 수 있다.

• 산업경영시스템학회지
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• 제44권3호
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• pp.33-38
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• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.

• 한국컴퓨터정보학회논문지
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• 제22권1호
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• pp.153-159
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• 2017

In recent years there has been a rise in the use and interest of the flipped learning as a teaching and learning paradigm. The flipped learning model includes any use of Internet technology to enrich the learning in a classroom, so that a professor can spend more time interacting with students instead of lecturing. In the flipped model, students viewed video lectures online outside of class time. Students then performed two kinds of assignments, a teamwork assignment and an individual work assignment, through the class time. In this paper, we propose a flipped educational model for a college class. This experimental research compares class of college algorithm using the flipped classroom methods and the traditional lecture-homework structure and its effect on student achievement. The result data of mid-term exam and final exam were analyzed and compared with previous year data. The findings of this research show that there was not a significant difference in the scores of student between two lecturing methods. The survey result and lecture evaluation by students show that students are in favor of the flipped learning.

• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.499-510
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• 2022

This study examines the reporting factors of crime against business in Korea and proposes a corresponding predictive model using machine learning. While many previous studies focused on the individual factors of theft victims, there is a lack of evidence on the reporting factors of crime against a business that serves the public good as opposed to those that protect private property. Therefore, we proposed a crime prevention model for the willingness factor of theft reporting in businesses. This study used data collected through the 2015 Commercial Crime Damage Survey conducted by the Korea Institute for Criminal Policy. It analyzed data from 834 businesses that had experienced theft during a 2016 crime investigation. The data showed a problem with unbalanced classes. To solve this problem, we jointly applied the Synthetic Minority Over Sampling Technique and the Tomek link techniques to the training data. Two prediction models were implemented. One was a statistical model using logistic regression and elastic net. The other involved a support vector machine model, tree-based machine learning models (e.g., random forest, extreme gradient boosting), and a stacking model. As a result, the features of theft price, invasion, and remedy, which are known to have significant effects on reporting theft offences, can be predicted as determinants of such offences in companies. Finally, we verified and compared the proposed predictive models using several popular metrics. Based on our evaluation of the importance of the features used in each model, we suggest a more accurate criterion for predicting var.

• 가정과삶의질연구
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• 제29권4호
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• pp.89-107
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• 2011

The purpose of this study was to develop Parent Education Program for Learning Coaching which enhances parent's learning-support behaviors, as well as, children's self-Efficacy and self-regulated learning capability, and to implement and evaluate the program for the families with school aged children. The results of this study were as follows. First, the contents of the experimental model of 'Parent Education Program for Learning Coaching' were specified as five factors namely; offering options, offering democratic rules, pursuing appropriate results, offering school-related information, offering self-regulated learning skills for children. Second, significant differences in the experiment group were found in pre- and post-test scores of parent's learning-support behaviors and children's self-efficacy and self-regulated learning capability, but not for the control group. Thus, based on these findings, a modified model of 'Parent Education Program for Learning Coaching' was presented as a conclusion.