• Communications of Mathematical Education
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• v.32 no.1
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• pp.23-36
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• 2018

Recently, we are working to utilize it in various fields with the expectation of the potential of artificial intelligence. There is also interest in applying to the field of education. In the field of education, machine learning and deep learning, which are used in artificial intelligence technology, are deeply interested in how to learn on their own. We are interested in how artificial intelligence and artificial intelligence technologies can be used in education and we have an interest in how artificial intelligence can be applied to mathematics education. The purpose of this study is to investigate the direction of mathematics education as the change of education paradigm and the development of artificial intelligence according to the development of information and communication technology. Furthermore, we examined how artificial intelligence can be applied to mathematics education.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.639-653
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• 2019

The purpose of this study is to design an instructional framework and cognitive learning environment for AI education based on computational thinking in order to ground the theoretical rationale for AI education. Based on the literature review, the learning model is proposed to select the algorithms and problem-solving models through the abstraction process at the stage of data collection and discovery. Meanwhile, the instructional model of AI education through computational thinking is suggested to enhance the problem-solving ability using the AI by performing the processes of problem-solving and prediction based on the stages of automating and evaluating the selected algorithms. By analyzing the research related to the cognitive learning environment for AI education, the instructional framework was composed mainly of abstraction which is the core thinking process of computational thinking through the transition from the stage of the agency to modeling. The instructional framework of AI education and the process of constructing the cognitive learning environment presented in this study are characterized in that they are based on computational thinking, and those are expected to be the basis of further research for the instructional design of AI education.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.237-239
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• 2021

본 논문에서는 YOLO 시스템을 사용하여 보조 보행 기구를 인식 한 후 자동문 속도 조절에 대한 방법을 제안한다. Visual studio, OpenCV, CUDA를 활용하여 보조 보행 기구를 인식이 가능하게 신경망 훈련 및 학습 한 데이터를 기반으로 Raspberry Pi, 카메라 모듈을 활용하여 실시간 모니터링을 통해 보조 보행 기구를 인식하여 자동문의 속도를 조절을 구현했다. 이로써 거동이 불편한 장애인은 원활하게 건물 출입이 가능하다.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.59-69
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• 2020

The purpose of this study is to design the framework of evaluation on learner's cognitive skill for artificial intelligence(AI) education through computational thinking. To design the rubric and framework for evaluating the change of leaner's intrinsic thinking, the evaluation process was consisted of a sequential stage with a) agency that cognitive learning assistance for data collection, b) abstraction that recognizes the pattern of data and performs the categorization process by decomposing the characteristics of collected data, and c) modeling that constructing algorithms based on refined data through abstraction. The evaluating framework was designed for not only the cognitive domain of learners' perceptions, learning, behaviors, and outcomes but also the areas of knowledge, competencies, and attitudes about the problem-solving process and results of learners to evaluate the changes of inherent cognitive learning about AI education. The results of the research are meaningful in that the evaluating framework for AI education was developed for the development of individualized evaluation tools according to the context of teaching and learning, and it could be used as a standard in various areas of AI education in the future.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.541-544
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• 2023

LLM(Largescale Language Model)의 성능 향상을 위한 비용 효율적인 방법으로 ChatGPT, GPT-4와 같은 초거대 모델의 output에 대해 SLM(Small Language Model)을 finetune하는 방법이 주목받고 있다. 그러나, 이러한 접근법은 주로 범용적인 지시사항 모델을 위한 학습 방법으로 사용되며, 제한된 특정 도메인에서는 추가적인 성능 개선의 여지가 있다. 본 연구는 특정 도메인(Writing Assistant)에서의 성능 향상을 위한 새로운 방법인 Self-Guided Approach를 제안한다. Self-Guided Approach는 (1) LLM을 활용해 시드 데이터에 대해 도메인 특화된 metric(유용성, 관련성, 정확성, 세부사항의 수준별) 점수를 매기고, (2) 점수가 매겨진 데이터와 점수가 매겨지지 않은 데이터를 모두 활용하여 supervised 방식으로 SLM을 미세 조정한다. Vicuna에서 제안된 평가 방법인, GPT-4를 활용한 자동평가 프레임워크를 사용하여 Self-Guided Approach로 학습된 SLM의 성능을 평가하였다. 평가 결과 Self-Guided Approach가 Self-instruct, alpaca와 같이, 생성된 instruction 데이터에 튜닝하는 기존의 훈련 방법에 비해 성능이 향상됨을 확인했다. 다양한 스케일의 한국어 오픈 소스 LLM(Polyglot1.3B, PolyGlot3.8B, PolyGlot5.8B)에 대해서 Self-Guided Approach를 활용한 성능 개선을 확인했다. 평가는 GPT-4를 활용한 자동 평가를 진행했으며, Korean Novel Generation 도메인의 경우, 테스트 셋에서 4.547점에서 6.286점의 성능 향상이 발생했으며, Korean scenario Genration 도메인의 경우, 테스트 셋에서 4.038점에서 5.795 점의 성능 향상이 발생했으며, 다른 유사 도메인들에서도 비슷한 점수 향상을 확인했다. Self-Guided Approach의 활용을 통해 특정 도메인(Writing Assistant)에서의 SLM의 성능 개선 가능성을 확인했으며 이는 LLM에 비용부담을 크게 줄이면서도 제한된 도메인에서 성능을 유지하며, LLM을 활용한 응용 서비스에 있어 실질적인 도움을 제공할 수 있을 것으로 기대된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.662-665
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• 2013

ITS(Intelligent Tutoring System) is a system that provides active and flexible tutoring conditions to learners, having adopted artificial intelligence to overcome the limitations of CAI(Computer Assisted Instruction). However, the existing ITS has a few problems; the system provides the same contents to every learner, not considering main variants of their learning and achievement, characters and levels, and therefore, it does not generate satisfactory results; the system does not offer a properly designed course schedule. Therefore, this thesis proposes ARS(Adaptive Recommendation System), founded on ITS, that provides contents designed based on the characters and levels of learners. To catch the characters of learners, the important variant for successful learning, ARS applies and embodies a module of self-assessment test. Also, it puts weighs according to the areas of learning which is different from the simplified assessment that asks for short and mechanical answers for the purpose of knowing the levels of the learners.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.182-196
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• 2024

This study introduces driving pattern analysis and change detection methods using smartphone sensors, based on contrastive learning. These methods characterize driving patterns without labeled data, allowing accurate classification with minimal labeling. In addition, they are robust to domain changes, such as different vehicle types. The study also examined the applicability of these methods to smartphones by comparing them with six lightweight deep-learning models. This comparison supported the development of smartphone-based driving pattern analysis and assistance systems, utilizing smartphone sensors and contrastive learning to enhance driving safety and efficiency while reducing the need for extensive labeled data. This research offers a promising avenue for addressing contemporary transportation challenges and advancing intelligent transportation systems.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1128-1131
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• 2021

COVID-19로 인한 홈 쿠킹 시장 수요 증가로 사람들은 더 편리한 요리 보조 시스템을 필요로 하고 있다. 기존 요리 시스템은 휴대폰, 책을 통해 레시피를 일방적으로 제공하기 때문에 사용자가 요리과정을 중단하고 반복적으로 열람해야 한다는 한계점을 가진다. '대화형 스마트 쿠킹 서비스' 시스템은 요리 과정 전반에서 필요한 내용을 사용자와 상호작용하며 적절하게 인지하고 알려주는 인공지능 시스템이다. Google의 MediaPipe를 사용해 사용자의 관절을 인식하고 모델을 학습시켜 사용자의 요리 동작을 인식하도록 설계했으며, dialogflow를 이용한 챗봇 기능을 통해 필요한 재료, 다음 단계 등의 내용을 실시간으로 제시한다. 또한 실시간 행동 인식으로 요리과정 중 화재, 베임 사고 등의 위험 상황을 감지하여 사용자에게 정보를 전달해줌으로써 사고를 예방한다. 음성인식을 통해 시스템과 사용자 간의 쌍방향적 소통을 가능하게 했고, 음성으로 화면을 제어함으로써 요리과정에서의 불필요한 디스플레이 터치를 방지해 위생적인 요리 환경을 제공한다.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.76-81
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• 2023

To distinguish cancer cells from normal cells, H&E (Hematoxylin & Eosin) staining is required. Pathological staining requires a lot of money and time. Recently, a digital dyeing method has been introduced to reduce such cost and time. In this paper, we propose a novel digital pathology algorithms. The first algorithm is the Pair method. This method learns the dyed phase image and unstained amplitude image taken by FPM (Fourier Ptychographic Microscopy) and converts it into a dyed amplitude image. The second algorithm is the unpair method. This method use the stained and unstained fluorescence microscopic images for modeling. In this study, digital staining was performed using a generative adversarial network (GAN). From the experimental results, we noticed that both the pair and unpair algorithms shows the excellent performance.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.8
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• pp.235-242
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• 2023

In recent years, as online counseling for infants and adolescents has increased, CNN-based deep learning models are widely used as assistance tools for emotion recognition. However, since most emotion recognition models are trained on mainly adult data, there are performance restrictions to apply the model to infants and adolescents. In this paper, in order to analyze the performance constraints, the characteristics of facial expressions for emotional recognition of infants and adolescents compared to adults are analyzed through LIME method, one of the XAI techniques. In addition, the experiments are performed on the male and female groups to analyze the characteristics of gender-specific facial expressions. As a result, we describe age-specific and gender-specific experimental results based on the data distribution of the pre-training dataset of CNN models and highlight the importance of balanced learning data.