• Cross-Cultural Studies
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• v.20
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• pp.29-66
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• 2010

This paper is a qualitative study of the experiences of Korean heritage language learners (KHLLs) with literacy (reading and writing), particularly before they enter the college-level heritage language classroom. Previous research, both qualitative and quantitative, has addressed the overall language background of KHLLs, including oral and aural proficiency and writing and reading ability, as well as demographic information (such as when the student immigrated to the United States) in relation to language test scores. This study addresses KHLL experiences in the following six areas as they relate to student perceptions and attitudes toward their own heritage language literacy: language proficiency, motivation for learning, academic preparedness, cultural connectedness, emotional factors, and social factors. Fourteen undergraduate students at a university in the western United States participated in a convenience sample by responding to a 10-question survey. Trends in responses indicated that KHLLs entered the classroom with high integrational motivation and experienced great satisfaction with perceived progress in literacy, but students also expressed regret for having missed childhood learning experiences that would likely have resulted in higher proficiency. These experiences include informal and formal instruction in the home and formal instruction outside of the home.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.65-70
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• 2020

CNN's technologies that represent emotional detection include primitive CNN algorithms, deployment normalization, and drop-off. We present the methods and data of the three experiments in this paper. The training database and the test database are set up differently. The first experiment is to extract emotions using Batch Normalization, which complemented the shortcomings of distribution. The second experiment is to extract emotions using Dropout, which is used for rapid computation. The third experiment uses CNN using convolution and maxpooling. All three results show a low detection rate, To supplement these problems, We will develop a deep learning algorithm using feature extraction method specialized in image processing field.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.61-68
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• 2021

The development of the fourth industry has enabled users to quickly share a lot of data online. We can analyze big data on information about tourist attractions and users' experiences and opinions using artificial intelligence. It can also analyze the association between characteristics of users and types of tourism. This paper analyzes individual characteristics, recommends customized tourist sites and proposes a system to provide the sacred texts of recommended tourist sites as AR services. The system uses machine learning to analyze the relationship between personality type and tourism type preference. Based on this, it recommends tourist attractions according to the gender and personality types of users. When the user finishes selecting a tourist destination from the recommendation list, it visualizes the information of the selected tourist destination with AR.

• Research in Mathematical Education
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• v.25 no.1
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• pp.21-43
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• 2022

Investigating the relationship between intrinsic and extrinsic motivation and their effects on affective mathematics engagement in a cultural context is critical for determining which types of motivation promote affective mathematics engagement and the relationship with cultural affordance. The investigation in the current study is comprised of two dependent studies. The results from Phase 1 indicate that attitude and emotion are better explained by extrinsic motivation, while self-acknowledgment and value are better explained by intrinsic motivation. The results of Phase 2 indicate that the Korean sample has greater extrinsic motivation, attitude, and emotion, while the U.S. sample has greater intrinsic motivation, self-acknowledgment, and value. The key outcome for this research is that disentangling cultural affordance from the emotional and cognitive structures is impossible.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.40-48
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• 2023

Speech Emotions recognition has become the active research theme in speech processing and in applications based on human-machine interaction. In this work, our system is a two-stage approach, namely feature extraction and classification engine. Firstly, two sets of feature are investigated which are: the first one is extracting only 13 Mel-frequency Cepstral Coefficient (MFCC) from emotional speech samples and the second one is applying features fusions between the three features: Zero Crossing Rate (ZCR), Teager Energy Operator (TEO), and Harmonic to Noise Rate (HNR) and MFCC features. Secondly, we use two types of classification techniques which are: the Support Vector Machines (SVM) and the k-Nearest Neighbor (k-NN) to show the performance between them. Besides that, we investigate the importance of the recent advances in machine learning including the deep kernel learning. A large set of experiments are conducted on Surrey Audio-Visual Expressed Emotion (SAVEE) dataset for seven emotions. The results of our experiments showed given good accuracy compared with the previous studies.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.471-474
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• 2022

음원 스트리밍 서비스 시장은 지속해서 성장해왔다. 그중 최근에 가장 성장세가 돋보이는 서비스는 Spotify와 Youtube music이다. 두 서비스의 추천시스템은 사용자가 좋아할 만한 음악을 계속해서 추천해 줌으로써 많은 사랑을 받고 있다. 추천시스템 성능은 추천에 활용할 수 있는 변수(Feature) 수에 비례한다고 볼 수 있다. 최대한 많은 정보를 알아야 사용자가 원하는 추천이 가능하기 때문이다. 본 논문에서는 기존에 존재하는 감정분류 방법론인 사전기반과 딥러닝 BERT를 사용한 머신기반 방법론을 적절하게 결합하여 장점을 유지하면서 단점을 보완한 하이브리드 감정 분석 모델을 제안함으로써 가사에서 느껴지는 감정 비율을 분석한다. 감정 비율을 음원 가중치 변수로 사용하면 감정 정보를 포함한 고도화된 추천을 기대할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.391-394
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• 2011

사용자의 감정을 자동으로 인식하는 연구는 사용자 중심의 서비스를 제공할 때 중요한 요소이다. 인간은 하나의 감정을 다양하게 분류하여 인식한다. 그러나 기계학습을 통해 감정을 인식하려고 할 때 감정을 단일값으로 취급하는 방법만으로는 좋은 성능을 기대하기 어렵다. 따라서 본 논문에서는 비감독 학습과 감독학습을 결합한 감정인식 모델을 제시하였다. 제안된 모델의 핵심은 비감독 학습을 이용하여 인간처럼 한 개의 감정을 다양한 하부 감정으로 분류하고, 이렇게 분류된 감정을 감독학습을 통해 성능을 향상 시키는 것이다.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.27-32
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• 2024

Reliable and fine-grained musical metadata are required for efficient search of rapidly increasing music files. In particular, since the primary motive for listening to music is its emotional effect, diversion, and the memories it awakens, emotion classification along with genre classification of music is crucial. In this paper, as an initial approach towards a "ground-truth" dataset for music emotion and genre classification, we elaborately generated a music corpus through labeling of a large number of ordinary people. In order to verify the suitability of the dataset through the classification results, we extracted features according to MPEG-7 audio standard and applied different machine learning models based on statistics and deep neural network to automatically classify the dataset. By using standard hyperparameter setting, we reached an accuracy of 93% for genre classification and 80% for emotion classification, and believe that our dataset can be used as a meaningful comparative dataset in this research field.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.175-181
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• 2021

The purpose of this study is to improve and develop the quality of non-face-to-face classes according to the types of presence by analyzing the effects of teaching presence and learning presence on the learning satisfaction of the non-face-to-face classes that have been suddenly conducted due to COVID-19. For this purpose, a survey on online classes of H University in Gwangju Metropolitan City was conducted to analyze learning satisfaction, teaching presence (learning design, direct promotion), and learning presence (cognitive presence, social presence). The results of the analysis showed that the learning contents of cognitive presence, which is a sub-factor of learning presence, were understood (=.589, p<.001), the direct promotion (=.420, p<.001), and the learning design (=.397, p<.01), which are the sub-factors of teaching presence, were influential in order.This means that the suddenly changed teaching method should have an attitude to improve the intimacy between the instructor and the fellow learners with positive emotional exchange or interaction. The instructor should try to overcome the limitations of time and space through blended learning that is both online and offline for high quality learning design, but the learning medium and learning method considering the physical fatigue of the learner should be developed.

• Journal of Oriental Neuropsychiatry
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• v.25 no.4
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• pp.333-350
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• 2014

Objectives: The purpose of this study was to research learning based on brain science and the learning methodology of Korean Medicine according to disparity of age. Through this, the study aimed to provide a guideline to related Korean Medicine treatments as well as the common nurturing/educational institutions. Methods: All journals and dissertations on brain science based learning methods studied in Korea to date that could be found in the National Assembly Library and the RISS were implemented in the analysis. The terminology used for search was as follows: 1st search, 'Brain'; 2nd search, 'Learning', 'Education'; 3rd search, 'Baby, 'Infant', 'Child'. For the learning methodology of Korean Medicine according to disparity of age, the related contents were extracted from Donguibogam and Liuyi, Sasang constitutional medicine. Results: A total of 30 studies, were collected as data. In the baby stage, the development and myelination of brain neurons are accelerated by experience and learning, highly influenced by social, cognitive and emotional movement. In infancy, the frontal lobe actively develops, so education for development of the prefrontal cortex is suggested. The brain of the infant at this stage can be developed by arts and physical education. In the child stage, the parietal and temporal lobe develop actively. Thus, programs to stimulate brain activity including brain respiration would be helpful in enhancing learning ability, concentration, etc. As evidence for learning and nurturing methodology according to disparity of age from Korean Medicine prospective, the following are listed: Location and time for sexual intercourse before pregnancy, stabilization during pregnancy, baby nurturing methods for nurturing from Donguibogam. Also Liuyi and Sasanag constitutional medicine can be the learning methodology according to disparity of age. And there are acupuncture points on each head section according to age in Donguibogam. Conclusions: Studies on 'brain-science based learning' are continuously being conducted. Based on these studies, diverse new brain-science based learning will be developed in the future. There is also a need to develop the learning methodology of Korean Medicine according to disparity of age in a more systematic and diverse way.