• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.109-117
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• 2021

Recently, the use of speech-based interfaces is increasing as a means for human-computer interaction (HCI). Accordingly, interest in post-processors for correcting errors in speech recognition results is also increasing. However, a lot of human-labor is required for data construction. in order to manufacture a sequence to sequence (S2S) based speech recognition post-processor. To this end, to alleviate the limitations of the existing construction methodology, a new data construction method called Back TranScription (BTS) was proposed. BTS refers to a technology that combines TTS and STT technology to create a pseudo parallel corpus. This methodology eliminates the role of a phonetic transcriptor and can automatically generate vast amounts of training data, saving the cost. This paper verified through experiments that data should be constructed in consideration of text style and domain rather than constructing data without any criteria by extending the existing BTS research.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.203-210
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• 2021

Yuri Vasilyev - actor, director and drama teacher. Russian meritorious artist, winner of the stage "Medal of Friendship" awarded by Russian President Vladimir Putin; academician of the Petrovsky Academy of Sciences and Arts in Russia, professor of the Russian National Academy of Performing Arts, and professor of the Bavarian Academy of Drama in Munich, Germany. The physiological sense stimulation method based on the improvement of voice, language and motor function of drama actors. On the basis of a systematic understanding of performing arts, Yuri Vasiliev created a unique training method of speech expression and skills. From the complicated art training, we find out the most critical skills for focused training, which we call basic skills training. Throughout the whole training process, Professor Yuri made a clear request for the actor's lines: "action! This is the basis of actors' creation. So action is the key! Action and voice are closely linked. Actor's voice is human voice, human life, human feeling, human experience and disaster. It is also the foundation of creation that actors acquire their own voice. What we are engaged in is pronunciation, breathing, tone and intonation, speed and rhythm, expressiveness, sincerity, stage voice and movement, gesture, all of which are used to train the voice of actors according to the standard of drama. In short, Professor Yuri's training course is not only the training of stage performance and skills, but also contains a rich view of drama and performance. I think, in addition to learning from the means and methods of training, it is more important for us to understand the starting point and training objectives of Professor Yuri's use of these exercises.

• Communications of Mathematical Education
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• v.36 no.4
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• pp.611-626
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• 2022

In this study, we introduced the case of college calculus course for vocational high school graduates with coding. We suggest this case as an alternative to overcome mathematics anxiety. Contents, python/SageMath codes, and textbook for this course, which help students to easily and quickly review middle and high school mathematics, were newly developed by authors. Due to the use of codes and chat with classmates in learning management system, most of the students who took this course reported that they no longer felt anxious in complex mathematics problems, had a full understanding of calculus concepts, could solve almost problems in any calculus textbooks with or without codes, and could explain calculus concepts to other students in their own words. In this way if mathematics and coding is properly used in mathematics education, it helps students with weak mathematical backgrounds or mathematics anxiety to restore confidence in mathematics in college. This could be applicable in secondary mathematics education.

• Korean Journal of Construction Engineering and Management
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• v.23 no.3
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• pp.45-55
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• 2022

BIM models allow building spaces to be instantiated and recognized as unique objects independently of model elements. These instantiated spaces provide the required semantics that can be leveraged for building code checking, energy analysis, and evacuation route analysis. However, theses spaces or rooms need to be designated manually, which in practice, lead to errors and omissions. Thus, most BIM models today does not guarantee the semantic integrity of space designations, limiting their potential applicability. Recent studies have explored ways to automate space allocation in BIM models using artificial intelligence algorithms, but they are limited in their scope and relatively low classification accuracy. This study explored the use of Graph Convolutional Networks, an algorithm exclusively tailored for graph data structures. The goal was to utilize not only geometry information but also the semantic relational data between spaces and elements in the BIM model. Results of the study confirmed that the accuracy was improved by about 8% compared to algorithms that only used geometric distinctions of the individual spaces.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.327-351
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• 2023

This paper aims to derive success factors that successfully lead an artificial intelligence (AI) project and prioritize importance. To this end, we first reviewed prior related studies to select success factors and finally derived 17 factors through expert interviews. Then, we developed a hierarchical model based on the TOE framework. With a hierarchical model, a survey was conducted on experts from AI-using companies and experts from supplier companies that support AI advice and technologies, platforms, and applications and analyzed using AHP methods. As a result of the analysis, organizational and technical factors are more important than environmental factors, but organizational factors are a little more critical. Among the organizational factors, strategic/clear business needs, AI implementation/utilization capabilities, and collaboration/communication between departments were the most important. Among the technical factors, sufficient amount and quality of data for AI learning were derived as the most important factors, followed by IT infrastructure/compatibility. Regarding environmental factors, customer preparation and support for the direct use of AI were essential. Looking at the importance of each 17 individual factors, data availability and quality (0.2245) were the most important, followed by strategy/clear business needs (0.1076) and customer readiness/support (0.0763). These results can guide successful implementation and development for companies considering or implementing AI adoption, service providers supporting AI adoption, and government policymakers seeking to foster the AI industry. In addition, they are expected to contribute to researchers who aim to study AI success models.

• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.139-147
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• 2019

This study conducted bibliometric analysis on studies of Korean intangible cultural heritage dance in the Seoul area and it aimed to figure out the tendencies of that research. For this, a list of Korean intangible cultural heritage dance studies of 24 events was collected and analysis was conducted through the big data analysis solution of TEXTOM. Text mining was used as the method for analysis. Research results showed that first, most of the studies were conducted on the Bongsan Talchum and studies on teaching and learning methods were especially actively conducted. On the other hand, there were not many studies on Gut and the need for research vitalization in that area was confirmed. Second, in studies on Cheoyongmu events, the term'contemporary Cheoyongmu' was used frequently. This can be considered the use of meaningful terms with regard to intangible cultural heritage dance that has changed throughout history. At this, the vitalization of research that can reveal the typicality of dance is demanded from research of other events as well. Third, there was a notable amount of research that compared and analyzed dance styles with regard to the Munmyoilmu. This was seen as the result of discussions in the Korean dancing world regarding archetypal dance styles expanding into academic discussions. Therefore, it was revealed that academic discussions can connect to academic outcomes apart from whether the matter is right or wrong.

• Korean small business review
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• v.41 no.3
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• pp.49-75
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• 2019

Market instability offers opportunities as well as the need for careful innovation strategies and learning for a company's survival. Companies that find new opportunities decide to carry out innovation and decide on the size of their investments by considering their position in the market they are aiming for and the intensity of competition. This study was conducted to check whether obstacles to innovation face by SMEs in the manufacturing sector vary depending on the stage of corporate growth and to identify the impact of the government support system on the decision-making process on the performance of innovation. According to the analysis, there were differences in obstacles to innovation depending on the stage of corporate growth. It was found that more innovative SMEs are, more obstacles they face, and to overcome such obstacles, they try to access government support systems more. In addition, the use of a government support system eliminated obstacles to innovation, and the positive and significant effects of investing in innovation were identified. This study is meaningful in that it explicitly approached these hypotheses by applying a multistage model to the process of innovation carried out by SMEs in the manufacturing sector.

• Educational Technology International
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• v.6 no.2
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• pp.23-41
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• 2005

One of the main factors that determine the quality of instruction is the teaching ability of the instructor administering the class. To evaluate teaching ability, methods such as peer review, student feedback, and teaching portfolio can be used. Among these, because feedback from the students is directly associated with how well the students feel they have learned, it is essential to improving instruction and teaching ability. The principal aim of instruction evaluation lies in the evaluation of instructor's qualification and the improvement of instruction quality by enhancing professionalism. However, the mandatory instruction evaluations currently being carried out at the term's end in universities today have limitations in improving instruction in terms of its evaluation items and times. To improve the quality of instruction and raise teaching abilities, instruction evaluations should not stop at simply being carried out but also be utilized as useful data for students and teachers. In other words, they need to be used to develop teaching and improve instruction for teachers, and consequently, should also exert a positive influence on students' scholastic achievements and learning ability. The most important thing in evaluation is the acquisition of accurate information and how to utilize it to improve instruction. The online instruction diagnosis item pool is a more realistic feedback device developed to improve instruction quality. The instruction diagnosis item pool is a cafeteria-like collection of hundreds of feedback questions provided to enable instructors to diagnose their instruction through self-diagnosis or students' feedback, and the instructors can directly select the questions that are appropriate to the special characteristics of their instruction voluntarily make use of them whenever they are needed. The current study, in order to find out if the online instruction diagnosis item pool is truly useful in reforming and improving instruction, conducted pre and post tests using 256 undergraduate students from Y university as subjects, and studied the effects of student feedback on instructions. Results showed that the implementation of instruction diagnosis improved students' responsibility regarding their classes, and students had positive opinions regarding the usefulness of online instruction diagnosis item pool in instruction evaluation. Also, after instruction diagnosis, analyzing the results through consultations with education development specialists, and then establishing and carrying out instruction reforms were shown to be more effective. In order to utilize the instruction diagnostic system more effectively, from planning the execution of instruction diagnosis to analyzing the results, consulting, and deciding how those results could be utilized to instruction, a systematic strategy is needed. In addition, professors and students need to develop a more active sense of ownership in order to elevate the level of their instruction.

• Korean Association of Arts Management
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• no.50
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• pp.5-31
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• 2019

The main purpose of this study was to use Deep Learning based Topic Modeling and Semantic Network Analysis to examine research trend of arts management-related papers in korea. For this purpose, research subjects such as 'The Journal of Cultural Policy', 'The Journal of Cultural Economics', 'The Journal of Culture Industry', 'The Journal of Arts Management', and 'The Journal of Human Content', which are the registered journal of the National Research Foundation of Korea directly or indirectly related to arts management field. From 1988 to 2017, a total of 2,110 domestic journals' signature, abstract, and keyword were analyzed. We tried Big Data analysis such as Topic Modeling and Semantic Network Analysis to examine changes in trends in arts management. The analysis program used open software R and standard statistical software SPSS. Based on the results of the analysis, the implications and limitations of the study and suggestions for future research were discussed. And the potential for development of convergent research such as Arts & Artificial Intelligence and Arts & Big Data.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.109-121
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• 2024

In various manufacturing processes such as textiles and automobiles, when equipment breaks down or stops, the machines do not work, which leads to time and financial losses for the company. Therefore, it is important to detect equipment abnormalities in advance so that equipment failures can be predicted and repaired before they occur. Most equipment failures are caused by bearing failures, which are essential parts of equipment, and detection bearing anomaly is the essence of PHM(Prognostics and Health Management) research. In this paper, we propose a preprocessing algorithm called SWT-SVD, which analyzes vibration signals from bearings and apply it to an anomaly transformer, one of the time series anomaly detection model networks, to implement bearing anomaly detection model. Vibration signals from the bearing manufacturing process contain noise due to the real-time generation of sensor values. To reduce noise in vibration signals, we use the Stationary Wavelet Transform to extract frequency components and perform preprocessing to extract meaningful features through the Singular Value Decomposition algorithm. For experimental validation of the proposed SWT-SVD preprocessing method in the bearing anomaly detection model, we utilize the PHM-2012-Challenge dataset provided by the IEEE PHM Conference. The experimental results demonstrate significant performance with an accuracy of 0.98 and an F1-Score of 0.97. Additionally, to substantiate performance improvement, we conduct a comparative analysis with previous studies, confirming that the proposed preprocessing method outperforms previous preprocessing methods in terms of performance.