• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 춘계학술발표대회
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• pp.331-334
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• 2021

In the latest eras, there has been a significant amount of development and research is done on the usage of Deep Learning (DL) for speech emotion recognition (SER) based on Convolutional Neural Network (CNN). These techniques are usually focused on utilizing CNN for an application associated with emotion recognition. Moreover, numerous mechanisms are deliberated that is based on deep learning, meanwhile, it's important in the SER-based human-computer interaction (HCI) applications. Associating with other methods, the methods created by DL are presenting quite motivating results in many fields including automatic speech recognition. Hence, it appeals to a lot of studies and investigations. In this article, a review with evaluations is illustrated on the improvements that happened in the SER domain though likewise arguing the existing studies that are existence SER based on DL and CNN methods.

• 한국과학교육학회지
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• 제42권4호
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• pp.477-486
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• 2022

본 연구에서는 상황 학습 이론과 실행 기반의 과학 교육에 관한 문헌들에 대한 고찰을 통해 과학적 실행 중심의 수업에서 과학 개념학습의 가능성을 살펴보았다. 먼저, 상황 학습 이론이 학생들의 과학적 실행에의 참여를 강조하는 최근 과학 교육 개혁의 흐름과 관련이 깊으며, 상황 학습의 관점에서 개념 학습은 학습자가 개념을 자원으로 활용하며 실행에 참여하는 동안 개념적 행위 주체성을 발현하여 개념을 지속적으로 발전시키는 과정을 통해 이루어진다는 것을 알 수 있었다. 또, 이러한 상황 학습 이론은 과학적 실행 기반의 수업에서 과학 개념을 학습하는 데에도 적용된다는 것을 확인하였다. 즉, 과학적 실행 중심의 과학 수업에서는 과학 개념이 자원으로 활용되며, 과학 개념을 자원으로 활용하는 동안 학생들은 과학적 실행에 더 잘 참여할 수 있고, 과학적 실천에의 참여는 개념적 행위 주체성의 발현을 통해 개념 학습을 더욱 촉진할 수 있다. 이러한 고찰의 내용이 학교 과학 교육에 주는 시사점을 논의하였다.

• 대한수학교육학회지:학교수학
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• 제2권1호
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• pp.53-72
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• 2000

The purpose of this study is to review theoretically the meaning and importance of mathematical communication and to search effective teaching-learning materials to develop it. This study has the content on relation of learning & communication, the qualitative examination of student's response in some mathematical communication tasks and materials of teaching mathematical communication.

• International Journal of Internet, Broadcasting and Communication
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• 제4권1호
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• pp.8-12
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• 2012

The purpose of this study is to review the possible effect of the learning paradigm shift from traditional method to ubiquitous learning. What are the societal issues that need to be address in order to design a new pedagogical platform trending from e-learning to m-learning and now the u-learning? That without the proper study of how learning environment may affect the learning process of an individual will lead to poor quality of education. This new era of learning environment offer a big opportunity for "anytime, anywhere" learning. Thus, Lifelong learning is at hand of everyone. Maximizing the benefit of new trend will be a great help and addressing the limitations will lead to quality education.

• 한국콘텐츠학회지
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• 제12권2호
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• pp.27-30
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• 2014

• 의학교육논단
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• 제23권1호
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• pp.1-2
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• 2021

• 품질경영학회지
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• 제26권3호
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• pp.17-30
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• 1998

Most authorities of the Total Quality Management.(TQM) movement emphasize learning and training as an integral part of TQM, and it is well accepted in practice. However, despite this strong normative emphasis on its role, learning and training has not been systematically addressed in the TQM literature for its contents and a, pp.oaches. I first review the concept of TQM, its principles and implementation process to indentify the needs for learning and training, and the nature of experiential learning and training. I then discuss the necessity and effectiveness of experiential learning and training in TQM.

• 한국시스템다이내믹스연구
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• 제15권4호
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• pp.129-159
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• 2014

This Study discuss exploring normative-prescriptive factors after the themes on Organizational learning categorize two descriptive/explanatory-perspectives, prescriptive/normative dimension. The former would contain information processing model, theory of action, organizing in organization, while Senge's suggestion on Learning Organization may compose the latter. Each perspective is reconstructed and reinterpreted into the causal mapping relationship founded on system thinking and SD. Underlying on the former try to discovery validities of the latter. But this study only put forward the integral-dynamic model of organizational learning without empirical simulation.

• 한국IT서비스학회지
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• 제17권2호
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• pp.165-177
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• 2018

Recently, online commerces are becoming more common due to factors such as mobile technology development and smart device dissemination, and online review has a big influence on potential buyer's purchase decision. This study presents a set of analytical methodologies for understanding the meaning of customer reviews of products in online transaction. Using techniques currently developed in deep learning are implemented Hierarchical Attention Network for analyze meaning in online reviews. By using these techniques, we could solve time consuming pre-data analysis time problem and multiple topic problems. To this end, this study analyzes customer reviews of laptops sold in domestic online shopping malls. Our result successfully demonstrates over 90% classification accuracy. Therefore, this study classified the unstructured text data in the semantic analysis and confirmed the practical application possibility of the review analysis process.

• Imaging Science in Dentistry
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• 제49권1호
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• pp.1-7
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• 2019

Purpose: Artificial intelligence (AI), represented by deep learning, can be used for real-life problems and is applied across all sectors of society including medical and dental field. The purpose of this study is to review articles about deep learning that were applied to the field of oral and maxillofacial radiology. Materials and Methods: A systematic review was performed using Pubmed, Scopus, and IEEE explore databases to identify articles using deep learning in English literature. The variables from 25 articles included network architecture, number of training data, evaluation result, pros and cons, study object and imaging modality. Results: Convolutional Neural network (CNN) was used as a main network component. The number of published paper and training datasets tended to increase, dealing with various field of dentistry. Conclusion: Dental public datasets need to be constructed and data standardization is necessary for clinical application of deep learning in dental field.