• 한국환경교육학회지:환경교육
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• 제21권4호
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• pp.94-110
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• 2008

Recently environmental problem becomes such a big issue all over the world that the necessity and importance of the environmental education in school has been simultaneously emphasized. While diverse methods for the environmental education have been researched, this paper, especially focused on a teaching-learning model called GBS (Goal-based scenario), aims to provide a new learner-centered approach for the environmental education. For this purpose, this paper first briefly presents two theoretical backgrounds of GBS (i.e., constructivism and Schank's dynamic memory theory), which is followed by specific and concrete strategies and methods of how to apply GBS in class for the teacher. GBS(Goal-Based Scenario) is a learner-centered model in which learners are presented with a reality-based scenario (or task or problem) and go through several stages of 'missions' to get to a final solution of the given scenario. GBS, while completely resonant with other constructivist learning models in terms of learner-centered approaches, is distinctive from others, when it supplies more specific, structured guides of learning, called 'missions', to the students throughout the whole learning process. In a words, GBS ought to be recognized as an unique learner-centered model compromising the contradictory concepts of 'learner control' and 'structure and specifics' in learning environments still without any damage of constructivist learning principles.

• 디지털산업정보학회논문지
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• 제18권4호
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• pp.55-65
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• 2022

The purpose of this study is to develop online educational contents and verify its effectiveness in order to strengthen the learning capabilities of college students. The theme of mind map online education contents is a mind map series for effective learning arrangement, and has been developed into a total of six contents. Each contents consisted of 20-30 minutes, and the details consisted of the concept, principle, learning case, how to write a mind map, and how to use a digital mind map. The results of the study are as follows. First, it was confirmed that the higher thinking ability of college students who took the mind map online education contents was improved. Second, it was confirmed that the self-directed learning attitude of college students improved after taking the mind map online education contents. Third, the reason why students' higher thinking ability and self-directed learning attitudes improved in this study is that they were developed in consideration of the composition of contents and appropriate video time. Therefore, in order to increase the effectiveness of online educational contents, it is necessary to examine specific cases using concepts from conceptual approaches to specific topics, and to faithfully reflect the procedure in which each learner can actually use the concept.

• 대한임베디드공학회논문지
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• 제19권2호
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• pp.107-114
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• 2024

Mobile ad hoc networks represent self-configuring networks of mobile devices that communicate without relying on a fixed infrastructure. However, traditional routing protocols in such networks encounter challenges in selecting efficient and reliable routes due to dynamic nature of these networks caused by unpredictable mobility of nodes. This often results in a failure to meet the low-delay and low-energy consumption requirements crucial for such networks. In order to overcome such challenges, our paper introduces a novel multi-objective and adaptive routing scheme based on the Q-learning reinforcement learning algorithm. The proposed routing scheme dynamically adjusts itself based on measured network states, such as traffic congestion and mobility. The proposed approach utilizes Q-learning to select routes in a decentralized manner, considering factors like energy consumption, load balancing, and the selection of stable links. We present a formulation of the multi-objective optimization problem and discuss adaptive adjustments of the Q-learning parameters to handle the dynamic nature of the network. To speed up the learning process, our scheme incorporates informative shaped rewards, providing additional guidance to the learning agents for better solutions. Implemented on the widely-used AODV routing protocol, our proposed approaches demonstrate better performance in terms of energy efficiency and improved message delivery delay, even in highly dynamic network environments, when compared to the traditional AODV. These findings show the potential of leveraging reinforcement learning for efficient routing in ad hoc networks, making the way for future advancements in the field of mobile ad hoc networking.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.199-208
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• 2023

The emergence of COVID-19 virus has shaken almost every aspect of human life including but not limited to social, financial, and economic changes. One of the most significant impacts was obviously healthcare. Now though the pandemic has been over, its aftereffects are still there. Among them, a prominent one is people lifestyle. Work from home, enhanced screen time, limited mobility and walking habits, junk food, lack of sleep etc. are several factors that have still been affecting human health. Consequently, diseases like diabetes, high blood pressure, anxiety etc. have been emerging at a speed never witnessed before and it mainly includes the people at young age. The situation demands an early prediction, detection, and warning system to alert the people at risk. AI and Machine learning has been investigated tremendously for solving the problems in almost every aspect of human life, especially healthcare and results are promising. This study focuses on reviewing the machine learning based approaches conducted in detection and prediction of diabetes especially during and post pandemic era. That will help find a research gap and significance of the study especially for the researchers and scholars in the same field.

• ETRI Journal
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• 제43권3호
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• pp.549-560
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• 2021

Cyberattacks are often difficult to identify with traditional signature-based detection, because attackers continually find ways to bypass the detection methods. Therefore, researchers have introduced artificial intelligence (AI) technology for cybersecurity analysis to detect malicious PowerShell scripts. In this paper, we propose a feature optimization technique for AI-based approaches to enhance the accuracy of malicious PowerShell script detection. We statically analyze the PowerShell script and preprocess it with a method based on the tokens and abstract syntax tree (AST) for feature selection. Here, tokens and AST represent the vocabulary and structure of the PowerShell script, respectively. Performance evaluations with optimized features yield detection rates of 98% in both machine learning (ML) and deep learning (DL) experiments. Among them, the ML model with the 3-gram of selected five tokens and the DL model with experiments based on the AST 3-gram deliver the best performance.

• Computers and Concrete
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• 제32권6호
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• pp.607-613
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• 2023

The objective of this work is to determine the compressive strength of geopolymer concrete utilizing four distinct machine learning approaches. These techniques are known as gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized trees (XRT), and deep learning (DL). Experimentation is performed to collect the data that is then utilized for training the models. Compressive strength is the response variable, whereas curing days, curing temperature, silica fume, and nanosilica concentration are the different input parameters that are taken into consideration. Several kinds of errors, including root mean square error (RMSE), coefficient of correlation (CC), variance account for (VAF), RMSE to observation's standard deviation ratio (RSR), and Nash-Sutcliffe effectiveness (NSE), were computed to determine the effectiveness of each algorithm. It was observed that, among all the models that were investigated, the GBM is the surrogate model that can predict the compressive strength of the geopolymer concrete with the highest degree of precision.

• 대한원격탐사학회지
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• 제37권6_3호
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• pp.1965-1974
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• 2021

위성 원격탐사 기법은 산림 모니터링에 적극적으로 활용될 수 있으며 우리나라 독자 운영 위성인 다목적실용위성을 활용하였을 때 특히 의미 깊다. 최근 들어 위성 원격탐사 자료에 머신러닝 기법을 적용함으로써 산림 모니터링을 수행하는 연구가 다수 이루어지고 있다. 머신러닝 기법을 통하여 제작된 산림모니터링 정보는 기존 산림 모니터링 방법의 효율성을 향상시키는 데에 활용할 수 있다. 머신러닝 기법의 경우 관심 지역과 활용 데이터의 특징에 따라 분류 정확도가 크게 달라지므로 다양한 모델을 적용함으로써 가장 효과적인 분류 결과를 도출하는 것이 매우 중요하다. 본 연구에서는 우리나라 삼척 지역에 대해 심층신경망을 적용함으로써 인공림과 자연림의 분류 성능을 확인하였다. 그 결과 픽셀 정확도가 약 0.857, F1 Score가 자연림과 인공림에 대해 각각 약 0.917과 0.433로 확인되었다. F1 score를 보았을 때 인공림의 분류 성능이 절대적으로는 낮은 수준을 나타냈다. 하지만 기존의 인공림과 자연림 분류 성능에 대해 F1 score를 기준으로 약 0.06, 그리고 0.10 향상된 성능을 확인할 수 있었다. 이러한 결과를 바탕으로 볼 때에 합성곱신경망 기반의 추가적인 모델을 적용함으로써 보다 적절한 모델을 분석할 필요가 있다.

• 한국과학교육학회지
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• 제31권4호
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• pp.491-504
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• 2011

과학영재 학생의 과학학습에 대한 개념을 알아보기 위해 COLS와 ALS 설문지를 이용하여 비교, 분석하였다. 과학영재 학생은 일반 학생과 과학학습에 대한 개념에 차이를 보이는데, 과학영재 학생은 과학학습을 과학 지식을 얻고, 과학 지식 구조를 확장해 나가고, 세상을 보는 새로운 관점을 얻는 활동이라고 인식한다. 과학영재 학생은 성별과 과목 선호도에 따라 과학학습에 대한 인식 및 접근방법에 의미 있는 차이는 보이지 않았다. 이 연구의 결과는 과학영재 수업을 위한 교재와 교수법에 도움이 될 것으로 예상한다.

• 디지털융복합연구
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• 제13권10호
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• pp.51-58
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• 2015

교육 분야에서의 빅데이터 활용이 선진국을 중심으로 확산되고 있다. 그러나 국내의 경우 이와 관련된 실험적 접근만이 있을 뿐 관련 연구나 현장의 서비스는 아직 나타나지 않고 있는 실정이다. 따라서 이러닝 업계에서 빅데이터의 응용이 저조한 이유를 파악하고 이를 개선할 연구와 대안 모색이 시급한 상황이다. 연구 결과, 이러닝 산업계에서는 빅데이터의 이해 수준이 높으면 빅데이터가 이러닝에 미치는 영향이 크다고 인식하고 있으며, 매출 규모가 큰 업체일수록 영향이 크다고 인식하고 있는 것으로 종합되었다. 이에 본 연구는 매출규모에 따라 다른 빅데이터에 관한 교육 및 활용 지원 정책을 펼 것을 제언하였다.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.29-36
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• 2022

In deep learning classification tasks, most models frequently assume that all labels are available for the training datasets. As such strategies to learn new concepts from unlabeled datasets are scarce. In fingerprint classification tasks, most of the fingerprint datasets are labelled using the subject/individual and fingerprint datasets labelled with finger type classes are scarce. In this paper, authors have developed approaches of classifying fingerprint images using the majorly known fingerprint classes. Our study provides a flexible method to learn new classes of fingerprints. Our classifier model combines both the clustering technique and use of deep learning to cluster and hence label the fingerprint images into appropriate classes. The K means clustering strategy explores the label uncertainty and high-density regions from unlabeled data to be clustered. Using similarity index, five clusters are created. Deep learning is then used to train a model using a publicly known fingerprint dataset with known finger class types. A prediction technique is then employed to predict the classes of the clusters from the trained model. Our proposed model is better and has less computational costs in learning new classes and hence significantly saving on labelling costs of fingerprint images.