• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.259-262
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• 2001

본 논문에서는 사용자의 취향에 맞춰 특정 웹 문서를 탐색하는 개인화된 웹 검색기의 구현을 다룬다. 사용자의 취향은 사용자의 직접적인 평가와 사용자의 검색 과정을 통해 얻어지는 간접적인 평가를 사용한 강화 학습을 사용하여 학습된다. 웹 문서의 검색은 사용자의 취향과 현재 문서와의 관련 도를 보상으로 사용한 강화 학습을 통하여 이루어진다.

• Journal of the Korean School Mathematics Society
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• v.13 no.1
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• pp.125-141
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• 2010

The aim of this paper is to develop a web-based Virtual Learning System for discrete mathematics learning using CAS (Computer Algebra System), The system contains a series of contents that are common between secondary und university curriculum in discrete mathematics such as sets, relations, matrices, graphs etc. We designed and developed web-based virtual learning contents contained in the proposed system based on Mathematia, webMathematica and phpMath taking advantages of rapid computation and visualization. The virtual learning system for discrete math provides movie lectures and 'practice mode' authored with phpMath in order to enhance conceptual understanding of each movie lesson. In particular, matrix learning is facilitated with conceptual diagram that provides interactive quizzes. Once the quiz results are submitted, Bayesian inference network diagnoses strong and weak parts of learning nodes for generating diagnostic reports to facilitate personalized learning. As part of formative evaluation, the overall responses were collected for future revision of the system with 10 university students.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.981-984
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• 2014

개인화된 서술형 수학 문제(mathematics word problem)는 오랫동안 연구된 분야로 학생들의 학업 성취도와 수학에 대한 태도에 관심을 가져왔다. 본 연구에서는 2013년 도입된 스토리텔링 수학에 개인화된 콘텐츠를 접목하여 그 효과를 알아보고자 하였다. 초등학생 26명을 대상으로 하여 약 110분 동안 수업을 진행하였으며, 무게에 대한 새로운 개념을 배우는 데 그 목적을 두었다. 각각 13명씩 개인화 그룹과 비 개인화 그룹으로 나누어 수업을 진행하였다. 학업 성취도(Learning Achievement)에서는 사전 시험(pre-test) 점수가 너무 높아 두 그룹 간에 서로간의 유의한 차이점을 발견하지 못했다. 수학에 대한 태도 부분과 몰입도(Flow) 부분에서는 다소 개인화 그룹의 값이 높았지만, 통계적으로 유의한 정도는 차이는 아니었다. 하지만 정성적 분석에서는 차이가 있었다. 개인화 그룹(Personalized group)은 비 개인화 그룹(non-personalized group)에 비해 개인화(personalization)가 수업의 재미있는 요소로서 보다 중요한 작용을 했다고 느꼈다. 또한, 테스트나 측정(measure) 부분에서 생겼던 문제점을 개선하여 재 실험이 있을 시엔 유의미한 값을 나타낼 것으로 기대된다.

• Progress in Medical Physics
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• v.31 no.3
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• pp.81-98
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• 2020

This review aims to provide a brief, comprehensive overview of advanced technologies of nuclear medicine physics, with a focus on recent developments from both hardware and software perspectives. Developments in image acquisition/reconstruction, especially the time-of-flight and point spread function, have potential advantages in the image signal-to-noise ratio and spatial resolution. Modern detector materials and devices (including lutetium oxyorthosilicate, cadmium zinc tellurium, and silicon photomultiplier) as well as modern nuclear medicine imaging systems (including positron emission tomography [PET]/computerized tomography [CT], whole-body PET, PET/magnetic resonance [MR], and digital PET) enable not only high-quality digital image acquisition, but also subsequent image processing, including image reconstruction and post-reconstruction methods. Moreover, theranostics in nuclear medicine extend the usefulness of nuclear medicine physics far more than quantitative image-based diagnosis, playing a key role in personalized/precision medicine by raising the importance of internal radiation dosimetry in nuclear medicine. Now that deep-learning-based image processing can be incorporated in nuclear medicine image acquisition/processing, the aforementioned fields of nuclear medicine physics face the new era of Industry 4.0. Ongoing technological developments in nuclear medicine physics are leading to enhanced image quality and decreased radiation exposure as well as quantitative and personalized healthcare.

• Food Science and Industry
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• v.54 no.3
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• pp.145-159
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• 2021

Conventional nutrition services involve producer-oriented approaches without considering the differences in the characteristics and circumstances of each individual, whereas personalized nutrition services are consumer-oriented concepts that provide products and services for maintaining optimal health conditions based on the genetic, physiological, and metabolic characteristics of individuals, with these products based on balanced nutrition and healthy living. Currently, methods for evaluating dietary habits, monitoring dietary behaviors, deep phenotyping, and metabotyping via microbiota profiling, as well as methods for predicting big data by using machine learning, have been previously studied in Korea and abroad. With the development of medical technology and the improvement of hygiene, the demand for personalized nutrition and health services for healthier, happier, and more satisfying lives is rapidly increasing. Therefore, based on scientific technologies, attempts are needed to advance these services into global personalized markets and to boost the global competitiveness of countries and companies.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.421-428
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• 2006

Intelligent Tutoring System(ITS), which offers individualized learning environment that consider many learners' variable, is realized by the effective alternative to take the place of domain expert. Accordingly, research on Learning Companion System(LC) is currently noticing. However, to develop LCS which applies effective interaction, it is necessary to combine several LCs, and personalized knowledge base have to be made first. Therefore, in this paper, we propose the 'Knowledge Base Object Medel', which is based on connectionist' in cognition structure, represents learner's knowledge to self-learnig object, and grows adaptive object by proprietor, verify the validity. This model lays the groundwork for design of personalized knowledge base, offers clue to development of adaptive ITS using knowledge base object.

• Smart Media Journal
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• v.9 no.1
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• pp.45-50
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• 2020

With the development of deep learning, studies using artificial neural networks based on deep learning in recommendation systems are being actively conducted. Especially, the recommendation system based on RNN (Recurrent Neural Network) shows good performance because it considers the sequential characteristics of data. This study proposes a travel route recommendation system using GRU(Gated Recurrent Unit) and Session-based Parallel Mini-batch which are RNN-based algorithm. This study improved the recommendation performance through an ensemble of top1 and bpr(Bayesian personalized ranking) error functions. In addition, it was confirmed that the RNN-based recommendation system considering the sequential characteristics in the data makes a recommendation reflecting the meaning of the travel destination inherent in the travel route.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.1-9
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• 2022

In the past few decades, great progress has been made on understanding the interaction between nutrition and health status. But despite this wealth of knowledge, health problems related to nutrition continue to increase. This leads us to postulate that the continuing trend may result from a lack of consideration for intra-individual biological variation on dietary responses. Precision nutrition utilizes personal information such as age, gender, lifestyle, diet intake, environmental exposure, genetic variants, microbiome, and epigenetics to provide better dietary advices and interventions. Recent technological advances in the artificial intelligence, big data analytics, cloud computing, and machine learning, have made it possible to process data on a scale and in ways that were previously impossible. A big data platform is built by collecting numerous parameters such as meal features, medical metadata, lifestyle variation, genome diversity and microbiome composition. Sophisticated techniques based on machine learning algorithm can be used to integrate and interpret multiple factors and provide dietary guidance at a personalized or stratified level. The development of a suitable machine learning algorithm would make it possible to suggest a personalized diet or functional food based on analysis of intra-individual metabolic variation. This novel precision nutrition might become one of the most exciting and promising approaches of improving health conditions, especially in the context of non-communicable disease prevention.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.274-276
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• 2002

With the proliferation of internet, it is increasingly needed to realize personalized news filtering service reflecting user's interest. In this Paper, we implement a filtering agent for Personalized news service. In the proposed system, Kohonen network for an unsupervised learning is used to train keywords provided by users and the personalization is achieved by using the trained neural network. After we trained and tested our filtering agent we could provide users news groups considering their interests.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.636-639
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• 2004

본 논문은 웹 도큐먼트를 기반으로 사용자에게 의미 있는 정보를 찾아주기 위한 연관 객체 추출 기법인 PMPL(Personalized Multi-Strategey Pattern Loaming) 시스템을 제안하고자 한다. PMPL 모듈은 인터넷의 정보를 여과하여 필터링하고, 사용자 개인화의 키워드를 중심으로 연관된 객체를 추출한다. 이때 연관된 객체 추출 시 대용량 데이터에서 시간적, 공간적면에서 효율적인 연관 탐색 기법인 Fp-Tree와 Fp-Growth 알고리즘을 적용시켰으며, 연관규칙 탐색을 보완하기 위해 가중치 기법인 만유인력 기법을 적용시켰다. PMPL 시스템을 실행한 결과 개인화된 사용자 중심어 기초로 기존의 단일 학습 기법에 비해 더 많은 의미 있는 연관 지식을 추출한 결과가 보였다.