• Korean Journal of Cognitive Science
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• v.31 no.4
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• pp.137-154
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• 2020

Psychological properties of learners have influence on learning behaviors in various ways. The purpose of this study was to examine how the goal orientation of learners affected the learning time distribution method. Regulatory focus and theories of intelligence were measured and manipulated in order to differentiate participants' goal-oriented state. Two variables are known to be key variables influencing learner's goal orientation, inducing the approach-avoidance strategy and mastery-performance oriented attitude. In the experiment, the control focus was divided into two groups based on the inclination test score (regulatory Focus Questionnaire, RFQ), and TOI(theory of intelligence) was temporally induced through manipulation to confirm the interaction between the two variables. Participants were able to determine the order of learning freely by learning a set of Spanish-Korean word pairs and then selecting the items they would like to re-learn. Word pairs consisted of difficult or easy items, and learners could learn the same word many times if they wanted to. In the results, promotion-incremental group showed allocating difficult word-pairs in early time.

• Journal of Korean Dental Hygiene Science
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• v.4 no.2
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• pp.9-18
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• 2021

Background: Based on the individualistic approach of experienced dental hygienists, this study attempted to provide basic data to find effective measures of human resource management by analyzing the correlation between organizational socialization and professional self-concept. Methods: Dental hygienists currently working in the Daegu area were evaluated. Nine questions related to duties, twenty-seven questions related to professional self-concept, and thirty-eight questions related to organizational socialization were included in the study. The reliability was professional self-concept (Cronbach's α = 0.859) and organizational socialization (Cronbach's α = 0.840). Results: Of the total 135 points for professional self-concept, the mean score of the participants was 62.67±8.45 points. In the sub-area, flexibility was the highest at 19.28±2.46 points, and communication was the lowest at 9.69±1.44 points. Of 190 points, organizational socialization averaged at 123.40±12.82 points. In the subarea, personal characteristics were the highest at 30.37±3.71 points, and occupational identity was the lowest at 10.34±1.94. Higher age (F=30.89, p<0.000), marital status (F=10.22, p<0.002), graduate or higher educational qualification (F=9.16, p<0.000), were associated with a higher position (F=20.62, p<0.000) and work experience (F=22.66, p<0.0000), when there was no intention to turnover (F=8.05, p<0.000). Organizational socialization was higher in participants with higher age (F=7.89, p<0.000), educational qualification (F=8.02, p<0.000), and position (F=5.12, p<0.007); higher work experience in general hospital (F=4.50, p<0.012); no intention to turnover (F=7.450, p<0.000); and no intention to turnover (F=24.46, p<0.000). Organizational socialization showed a significant positive correlation with professional self-concept (r=0.721, p<0.000); job performance and skills (r=0.615, p<0.000) and organizational commitment and satisfaction (r=0.610, p<0.000) showed a high positive correlation. Turnover intention (β=0.213, p<0.000) was found to have a significant effect on organizational socialization. Leadership (β=0.168, p<0.05) and satisfaction (β=0.483, p<0.000) were found to have a significant effect. The total explanatory power of this variable was 62.7%. Conclusion: To ensure successful organizational socialization, human resource management should be performed through regular verification, which can result in improved quality of dental care services.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.263-278
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• 2022

With the recent advent of IoT technology, automatic pet feeders are being distributed so that owners can feed their companion animals while they are out. However, due to behaviors of pets, the method of measuring weight, which is important in automatic feeding, can be easily damaged and broken when using the scale. The 3D camera method has disadvantages due to its cost, and the 2D camera method has relatively poor accuracy when compared to 3D camera method. Hence, the purpose of this study is to propose a deep learning approach that can accurately estimate weight while simply using a 2D camera. For this, various convolutional neural networks were used, and among them, the ResNet101-based model showed the best performance: an average absolute error of 3.06 grams and an average absolute ratio error of 3.40%, which could be used commercially in terms of technical and financial viability. The result of this study can be useful for the practitioners to predict the weight of a standardized object such as feed only through an easy 2D image.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.89-100
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• 2021

Comedy has been popular since ancient Greece. In order to visualize comedy more effectively, the actor's acting acts as an important factor. Then active discussion is needed on how actors can actually shape their comedic performance. I would like to approach comedic acting methods, focusing on the character of Kim Seo-young in the play . This researcher played the character of Kim Mi-young, and the characteristics of comedic acting include exaggeration, repetition, fast tempo, changing tone, and exaggerated physical behavior. Comedic acting comes from a dissonance of reactions. This is because unexpected reactions to stimuli can cause audience laughter. Comedic acting is also important in exaggeration and repetition, which must be based on true acting. The fast tempo of the act and the changing tone of the words also affect comedic acting expressions, and the embodiments of 'slapstick' and 'group dance', which are characteristics of farce acting, play an important role in causing audience laughter. In order for these characteristic elements to show comic effects, the actor's true acting must be the basis. What is important in comedic acting is understanding the narrative flow and features of the text and expressing it accurately. Comedic effects can be sufficiently represented if an actor truly expresses his means and faithfully demonstrates what the text requires. It is hoped that such research will help explore various acting arts, the acting education field, and the theater creation process.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.148-153
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• 2022

This thesis investigates the meaning of a performer's energy taking into an account of the full bodily engagement as the flow of energy and/or psychophysical readiness focusing specifically on the significance of qualitative bodily transformation. In this contemporary era, the dominance of performer training and its approaches to acting/training has very frequently meant that how to play a character in a textual based approach by emphasizing on interpreting and impersonating the role as real as possible. In this sense, as a performer trainer, from my observation and research findings shows that it is common for the term energy is not to be motivated by what a performer's body needs within a specific moment in specific performance which they are working on. To address the problematic issues, this thesis begins by interrogating the practical meaning of transformation with addressing the principle and process of movement by means of the flow of energy on stage. For a performer, inhabiting/integrating his/her body and mind as oneness and/or unity means s/he sincerely encounter, confront, and therefore listen to his/her body in here and now. Because since the performer's physical appearance completely defined his/her psychological state, no one can play either the past or the future in the moment. In this manner, an appropriate use of energy synonymous with the flow of energy correspondence with the given time and space in which the performer's body informs and initiates movement as necessary action. To be precise, the performer's bodily movement either visible or invisible in a sense of training and rehearsal is perceived as attaining or achieving psychophysical involvement as the full body engagement which enable to make the event happen in the right moment. Here, this thesis argues that the significance of a performer's inner intensity reminds us of the necessity of qualitative transformation on which the performer could discover his/her own mode of awareness as well as a way his/her body function in the given circumstance. From this point of view, this research finding would advocates that the performer's body maintains in the field of energy flow where his/her conscious effort and/or mindfulness disappear. The performer's movement is a manifestation of the whole bodily engagement by means of being as real in that moment rather than representing reality.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.99-132
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• 2022

The purpose of this study is to propose AET (Augmented and Extended Topics), a novel method of synthesizing both LDA and BERTopic results, and to analyze the recently published LIS articles as an experimental approach. To achieve the purpose of this study, 55,442 abstracts from 85 LIS journals within the WoS database, which spans from January 2001 to October 2021, were analyzed. AET first constructs a WORD2VEC-based cosine similarity matrix between LDA and BERTopic results, extracts AT (Augmented Topics) by repeating the matrix reordering and segmentation procedures as long as their semantic relations are still valid, and finally determines ET (Extended Topics) by removing any LDA related residual subtopics from the matrix and ordering the rest of them by F₁ (BERTopic topic size rank, Inverse cosine similarity rank). AET, by comparing with the baseline LDA result, shows that AT has effectively concretized the original LDA topic model and ET has discovered new meaningful topics that LDA didn't. When it comes to the qualitative performance evaluation, AT performs better than LDA while ET shows similar performances except in a few cases.

• Geophysics and Geophysical Exploration
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• v.26 no.3
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• pp.126-137
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• 2023

In this study, a recurrent neural network (RNN) was employed as a methodological approach to classify dolphin click signals derived from ocean monitoring data. To improve the accuracy of click signal classification, the single time series data were transformed into fractional domains using fractional Fourier transform to expand its features. Transformed data were used as input for three RNN models: long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (BiLSTM), which were compared to determine the optimal network for the classification of signals. Because the fractional Fourier transform displayed different characteristics depending on the chosen angle parameter, the optimal angle range for each RNN was first determined. To evaluate network performance, metrics such as accuracy, precision, recall, and F1-score were employed. Numerical experiments demonstrated that all three networks performed well, however, the BiLSTM network outperformed LSTM and GRU in terms of learning results. Furthermore, the BiLSTM network provided lower misclassification than the other networks and was deemed the most practically appliable to field data.

• Journal of Korea Entertainment Industry Association
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• v.13 no.2
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• pp.93-100
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• 2019

Dynamic Compressors in vocal recordings of modern pop music are essential equipment. Dynamic compressors are applied not only to the mix for listening to music but also to the monitor for the singer to listen to his voice along with the accompaniment while the singer is recording. This study is an experimental study on the effects of a dynamic compressor applied to a monitor environment on the vocal performance of a singer. 10 participating singers participated in the blind test to test how the vocals heard through the monitor would be affected by the 1:1, 2:1 and 4:1 compression ratio. Experimental results show that the higher the compression ratio applied to the monitor, the bigger the song, the brighter the tone, but the pitch becomes finer inaccuracy on the bigger dynamic part of the song. In post-interviews with blinds, it was found that singers generally preferred to hear compressed sound through a compressor on the monitor. Since the music used in the experiment was a ballad with a wide dynamic range, it could not be generalized to all kind of music recordings, but it could provide important implications for the monitoring of recording sites. In addition, We hope that the cognitive science approach to recording technology will be added based on this paper which has been studied through empirical studies on the effect of the monitor environment on the singing voice.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.593-601
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• 2017

In this paper, we constructed the model of production/investment, distribution, and screening by using variables that can be considered at each stage according to the value chain stage of the movie industry. To increase the predictive power of the model, a regression analysis was used to derive meaningful variables. Based on the given variables, we compared the difference in predictive power between the artificial neural network, which is a machine learning analysis method, and the decision tree analysis method. As a result, the accuracy of artificial neural network was higher than that of decision trees when all variables were added in production/ investment model and distribution model. However, decision trees were more accurate when selected variables were applied according to regression analysis results. In the screening model, the accuracy of the artificial neural network was higher than the accuracy of the decision tree regardless of whether the regression analysis result was reflected or not. This paper has an implication which we tried to improve the performance of movie prediction model by using machine learning analysis. In addition, we tried to overcome a limitation of linear approach by reflecting the results of regression analysis to ANN and decision tree model.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.481-492
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• 2023

This study explores how to build a Korean dataset to extract information from text using generative large language models. In modern society, mixed information circulates rapidly, and effectively categorizing and extracting it is crucial to the decision-making process. However, there is still a lack of Korean datasets for training. To overcome this, this study attempts to extract information using text-based zero-shot learning using a generative large language model to build a purposeful Korean dataset. In this study, the language model is instructed to output the desired result through prompt engineering in the form of "system"-"instruction"-"source input"-"output format", and the dataset is built by utilizing the in-context learning characteristics of the language model through input sentences. We validate our approach by comparing the generated dataset with the existing benchmark dataset, and achieve 25.47% higher performance compared to the KLUE-RoBERTa-large model for the relation information extraction task. The results of this study are expected to contribute to AI research by showing the feasibility of extracting knowledge elements from Korean text. Furthermore, this methodology can be utilized for various fields and purposes, and has potential for building various Korean datasets.