• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.309-314
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• 2023

This study examines the development and limitations of current artificial intelligence (AI) music composition programs. AI music composition programs have progressed significantly owing to deep learning technology. However, they possess limitations pertaining to the creative aspects of music. In this study, we collect, compare, and analyze information on existing AI-based music composition programs and explore their technical orientation, musical concept, and drawbacks to delineate future directions for AI music composition programs. Furthermore, this study emphasizes the importance of developing AI music composition programs that create "personalized" music, aligning with the era of personalization. Ultimately, for AI-based composition programs, it is critical to extensively research how music, as an output, can touch the listeners and implement appropriate changes. By doing so, AI-based music composition programs are expected to form a new structure in and advance the music industry.

• Journal of Korean Elementary Science Education
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• v.42 no.3
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• pp.467-479
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• 2023

For the successful implementation of education using artificial intelligence (AI) in schools, the perception of teachers is important. This study focuses on elementary school teachers and their perception of the need and teaching efficacy of science classes using AI before and after participating in a research school program. The analysis explores four key aspects, namely, learning, teaching, assessment, and communication. The study recruited 24 elementary school teachers from a school designated by the Gangwon Provincial Office of Education to participate in a year-long research school program. The study collected data using pre- and post-program surveys to explore changes in the perception of teachers regarding AI-based science classes. Furthermore, the researchers conducted individual in-depth interviews with four elementary school teachers to investigate the experience factors that influenced the changes in their perception of the aforementioned classes. The main findings were as follows. First, elementary school teachers were positively aware of the need for science classes using AI even prior to their research school experience; this perception remained positive after the research school program. Second, the science teaching efficacy of the elementary school teachers using AI was generally moderate. Even after the research school experience, the study found no statistically significant increase in efficacy in teaching science using AI. Third, by analyzing the necessity-efficacy as quadrants, the study observed that approximately half of the teachers who participated in the research school reported positive changes in learning, teaching, and assessment. Fourth, the study extracted four important experience factors that influenced the perception of the teachers of science classes using AI, namely, personal background and characteristics, personal class practice experience, teacher community activities, and administration and work of school. Furthermore, the study discussed the implications of these results in terms of the operation of research schools and science education using AI in elementary schools.

• Journal of Physiology & Pathology in Korean Medicine
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• v.32 no.6
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• pp.370-374
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• 2018

Oriental Diagnosis System(ODS) is an artificial intelligence program that utilize entered diagnosis knowledge, determine patient's disease and decide right medicine. The purpose of this study is to find a correlation between pattern Identification in Korean medicine and each sasang types(Tae-Eum and So-Yang) by analyzing ODS diagnosis result. Eventually our study secure availability of using ODS program at clinical training or developing diagnosis program. Subject of this study is 50 patients who was performed Sasang constitution diagnosis (28 patients were Tae-Eum and 22 patients were So-Yang). We analyize patient's diagnosis records by using ODS program and obtained result about pattern Identification. We used SPSS statistics 23 in analyzing the differences of the scores of Eight Principle Pattern Identification, Qi-Blood Pattern Identification, and Bing-xie Pattern Identification in each Sasang types (Tae-Eum, So-Yang). The Heat and Heat-moisture scores were significantly different(p<0.05) and Qi-Blood Pattern Identification scores were not different in each Sasang types(p>0.05). And Weight was significantly different in each Sasang types(p<0.05). It is hard to generalize the result because subject of this study was not enough and had sample speciality(tinnitus patients). However, we explained correlation between pattern Identification in korean medicine and each sasang types based on quantifiable and objective evidence system. it can be used at education of korean medicine and evidence of practice diagnosis. Futhermore, there have been no studies about anaylizing correlation between pattern Identification in Korean medicine and each sasang types using ODS program. So it is worthy of being utilized at clinical evidence data of ODS program.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.34 no.1
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• pp.77-84
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• 2024

Objectives: Interventions promoting physical exercise and healthy habits in workplaces have been shown to be effective in reducing risk factors for metabolic syndrome. This study was conducted to examine the effects of an individualized conditioning exercise program of IT company office workers with or at higher risk of metabolic syndrome. Methods: A total of 444 IT company office workers with or at higher risk of metabolic syndrome participated in a 3-month conditioning exercise program. Body composition data using bioelectrical impedance analysis and cardiopulmonary data using cardiopulmonary exercise testing from 53 individuals (mean age: 34.8 ± 7.1 years, sex : 21% female, height : 170.4 ± 6.8 cm, weight : 75.2±12.2 kg, body mass index : 25.8±3.3 kg/m²) who have successfully completed pre-test, intervention, and post-test were analyzed. The 12 weeks intervention encompassed: (1) health counseling (2) supervised exercise(endurance-based, aerobic exercise, or circuit training once a week for 50 minutes at heart rate reserve(HRR) of 77-95%) (3) self-directed exercise and biweekly health screening checks. Results: The results indicated a significant decrease in body weight, body fat mass and body mass index, respectively. Moreover, VO₂peak, AT VO₂ and AT Time significantly improved, respectively. Resting blood pressure(SBP/DBP) showed positive changes but were not statistically significant. We observed the correlation between characteristics of participants and rate of changes in cardiopulmonary outcomes of participants, there are no significant correlation. These results indicate positive changes in body composition and cardiorespiratory fitness parameters following individualized conditioning exercise program. Conclusions: Individualized workplace exercise program for preventing metabolic syndrome can lead to improvements in body composition and cardiorespiratory fitness.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• The Journal of Korean Association of Computer Education
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• v.23 no.4
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• pp.49-59
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• 2020

At the beginning of the discussion of AI education in K-12 education, the study was conducted to develop and apply an AI education program based on Design Thinking and analyze the effects of the AI education programs. In the AI education program, students explored and defined the AI problems they were interested in, gathered the necessary data to build an AI model, and then developed a project using scratch. In order to analyze the effectiveness of the AI education program, the change of learner's perception of the value of AI and the change of AI efficacy were analyzed. The overall perception of the AI project was also analyzed. As a result, AI efficacy was significantly increased through the experience of carrying out the project according to the Design Thinking process. In addition, the efficacy of solving problems with AI was influenced by the level of use of programming languages. The learner's overall perception of the AI project was positive, and the perceptions of each stage of the AI project (AI problem understanding and problem exploration, practice, problem definition, problem solving idea implementation, evaluation and presentation) was also positive. This positive perception was higher among students with high level of programming language use. Based on these results, the implications for AI education were suggested.

• Journal of Physiology & Pathology in Korean Medicine
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• v.33 no.5
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• pp.255-260
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• 2019

Oriental Diagnosis System(ODS) is an artificial intelligence program that utilize entered diagnosis knowledge, determine patient's disease and decide right medicine. The purpose of this study is to find a correlation between pattern Identification in Korean medicine and each sasang types(So-Yang, So-Eum and Tae-Eum) by analyzing ODS diagnosis result. Eventually our study secure availability of using ODS program at clinical training or developing diagnosis program. Subject of this study is 32 students participating in Sasang medical practice(12 subjects were So-Yang, 7 subjects were So-Eum, and 13 subjects were Tae-Eum). We analyze subject's clinical practice result reports by using ODS program and obtained result about pattern Identification. We used SPSS statistics 23 in analyzing the differences of the scores of Eight Principle Pattern Identification, Qi-Blood Pattern Identification, Bing-xie Pattern Identification, and Visceral Pattern Identification in each Sasang types (So-Yang, So-Eum, Tae-Eum). In the case of Heat-moisture, Tae-Eum showed higher score than So-Eum, but So-Yang showed no difference from the other two Sasang types(p<0.05). And in the case of Food-accumulation, Tae-Eum and So-Yang showed significantly higher score than So-Eum(p<0.05). It is hard to generalize the result because subject of this study was not enough. However, we explained correlation between pattern Identification in korean medicine and each sasang types based on quantifiable and objective evidence system. Therefore use of ODS program in student clinical practice training help to understand the relationship and correlation between different pattern Identification and will help standardization of clinical practice education.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.10-21
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• 2022

In order to improve formal presentation attitudes such as presentation of job interviews and presentation of project results at the company, there are few automated methods other than observation by colleagues or professors. In previous studies, it was reported that the speaker's stable speech and gaze processing affect the delivery power in the presentation. Also, there are studies that show that proper feedback on one's presentation has the effect of increasing the presenter's ability to present. In this paper, considering the positive aspects of correction, we developed a program that intelligently corrects the wrong presentation habits and attitudes of college students through facial analysis of videos and analyzed the proposed program's performance. The proposed program was developed through web-based verification of the use of redundant words and facial recognition and textualization of the presentation contents. To this end, an artificial intelligence model for classification was developed, and after extracting the video object, facial feature points were recognized based on the coordinates. Then, using 4000 facial data, the performance of the algorithm in this paper was compared and analyzed with the case of facial recognition using a Teachable Machine. Use the program to help presenters by correcting their presentation attitude.

• Journal of Science Education
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• v.42 no.1
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• pp.12-26
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• 2018

This study examined the effects of SSI argumentation program on the preservice biology teachers' decision-making types and communication ability. The SSI argumentation program was developed based on 'Social Decision-Making & Problem-Solving strategy' and Toulmin's argumentation pattern. The preservice teachers had opportunities of SSI argumentation through small group discussions. They were asked to identify the issues regarding SSI, think of solutions, and make a decision along with claims, warrants, data, and rebuttals. The preservice biology teachers experienced four SSI topics of abortion, euthanasia, gene manipulation, artificial intelligence. The results indicated that the preservice biology teachers significantly improved the communication ability after the intervention, but they did not change their types of decision-making. In addition, after the intervention, the Pearson correlation results indicated that 'the logical type' of decision-making significantly relates to the communication ability(p<.01). The preservice biology teachers mentioned that they improved their ability of considering warrants, data, background information, context, and rebuttals. Further, the preserivce biology teachers mentioned that they became take an interest in socioscientific issues and improved their ability of accepting criticism from others as well as caring about others when they argue each other. This study implicated that the SSI argumentation program has effects on improving personality education in school science.

• Journal of the Korean Geotechnical Society
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• v.20 no.7
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• pp.69-78
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• 2004

In this study, the back analysis program was developed by applying the genetic algorithm, one of artificial intelligence fields, to the direct method. The optimization process which has influence on the efficiency of the direct method was modulated with genetic algorithm. On conditions that the displacement computed by forward analysis for a certain rock mass model was the same as the displacement measured at the tunnel section, back analysis was executed to verify the validity of the program. Usefulness of the program was confirmed by comparing relative errors calculated by back analysis, which is carried out under the same rock mass conditions as analysis model of Gens et at (1987), one of back analysis case in the past. We estimated the total displacement occurring by tunnelling with the crown settlement and convergence measured at the working faces in three tunnel sites of Kyungbu Express railway. Those data measured at the working face are used for back analysis as the input data after confidence test. As the results of the back analysis, we comprehended the tendency of tunnel behaviors with comparing the respective deformation characteristics obtained by the measurement at the working face and by back analysis. Also the usefulness and applicability of the back analysis program developed in this study were verified.