• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.105-112
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• 2021

In the era of big data, interest in data is exploding. In particular, the development of the Internet and social media has led to the creation of new data, enabling the realization of the era of big data and artificial intelligence and opening a new chapter in convergence technology. Also, in the past, there are many demands for analysis of data that could not be handled by programs. In this paper, an analysis model was designed and verified for classification of unstructured data, which is often required in the era of big data. Data crawled DBPia's thesis summary, main words, and sub-keyword, and created a database using KoNLP's data dictionary, and tokenized words through morpheme analysis. In addition, nouns were extracted using KAIST's 9 part-of-speech classification system, TF-IDF values were generated, and an analysis dataset was created by combining training data and Y values. Finally, The adequacy of classification was measured by applying three analysis algorithms(random forest, SVM, decision tree) to the generated analysis dataset. The classification model technique proposed in this paper can be usefully used in various fields such as civil complaint classification analysis and text-related analysis in addition to thesis classification.

• Journal of The Korean Association For Science Education
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• v.41 no.3
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• pp.183-192
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• 2021

As part of the second-year monitoring study on the implementation of the 2015 revised science curriculum, this study investigated high school science teachers' perception and realization of instruction and assessment of elective courses to derive measures to settle and improve the science curriculum. A total of 244 high school science teachers responded to the survey questionnaire, and 9 teachers participated in interviews. In survey results, science teachers are contemplating ways to increase students' science competencies and their participation in classes, but still, lecture-oriented classes are most often used in their teaching. Regarding assessment, teachers responded that there were positive changes in all of the questions related to process-based assessment (PBA). Regarding the difficulty of managing science elective courses, teachers most often selected increased numbers of subjects being covered, overload of work, and the burden of restructuring classes considering various ways of teaching and assessment. Through in-depth interviews, teachers argued the difficulty for Science I courses to emphasize student participatory classes compared to integrated science, and the difficulty to implement student participatory classes for Science II courses, which are mainly placed in the third grade. Teachers also argue that it is necessary to secure time to implement PBA in science elective courses, and that there is no need to implement PBA for the science experiment since there are no tests on the SAT. Based on the results of the study, discussed in the conclusion are support plans for the settlement of PBA in elective courses, and the need for in-depth analysis of the direction and cause of student participatory classes and PBA at the school.

• Journal of the Korean earth science society
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• v.40 no.5
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• pp.538-547
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• 2019

This study analyzes the conceptual understanding of in-service and pre-service earth science teachers about the H-R diagrams and evolution of stars using conceptual status analysis categories. The results show that (a) many teachers use unscientific language in the Intelligibility range, (b) teachers are categorized in Low scientific inquiry ability related to graph creation and unscientific analogy for scientific concept which is hightly corelated to the possibility of misunderstanding in the teaching process, and (c) pre-service teachers lack the understanding of the secondary science curriculum. It is necessary to develop pre-service curriculum that can be applied to the school site. In the category of Plausibility range, (d) both groups understood the cosmological meaning of stellar evolution. However, pre-service teachers do not specifically explain the mechanism of a star. In the category of Fruitfulness range, in-service teachers come up with educational problems reflecting the academic characteristics of earth science and apply their knowledge to actual problem solving. On the other hand, pre-service teachers show high nonresponse ratio, they do not see the H-R diagram and the evolution of stars as a practical concept. In the analysis process, both groups are found to have many unscientific conceptions about the H-R diagram and evolution of stars. Therefore, it is suggested that caution be used in developing a professional development program of earth science teachers.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.249-260
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• 2021

This study is a meta-analysis study conducted to integrate and analyze the results of flip-learning studies for Korean nursing students. We searched PubMed, EMBASE, Cochrane, CINAHL, and Korean databases. Randomized controlled trials (RCTs) and Non-Randomized controlled trials (Non-RCTs) evaluating the effects of flipped learning for Korean nursing students were included. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated using a random-effects meta-analysis. The entire effect size in flipped learning was big in effect size (SMD = 1.21; 95% CI = 0.84 to 1.63; I2 = 93.9; n = 23) compared to the control groups. The analysis results of subgroups according to the classification of Bloom showed that flipped learning was found to have a significant effect on psychomotor domain, cognitive domain, and affective domain. A total of 10 literature analyses, this meta-analysis showed that flipped learning on Korean nursing students is effective in psychomotor, cognitive, and affective domain than the traditional teaching method. The flip learning can be integrated into theoretical and practical nursing education to improve the academic performance of nursing students.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.291-299
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• 2020

The purpose of this study was to be used as basic data for the development of future-type curriculum in health. The awareness and preparation of the forth industrial revolution were surveyed on 280 college students in health departments preparing medical technicians. A self-written structured questionnaire was used for data collection, and the recognition of the forth industry revolution was 2.74, 3D printing (3.59) was high, and neural network machine learning(2.33) was the lowest. Students majoring in Physiotherapy (3.00) had the highest perception, and those majored in Dental engineering(2.37) had the lowest perception, and there was a difference in the degree of perception of IoT by major (p=0.024). For the forth industrial revolution, 54.5% of students are preparing, and lack of interest (42.9%) is the most difficult reason to prepare, and 50.6% of educational experience and 60.9% of VR&AR game experience have experience. In the era of the forth industrial revolution, job loss (38.7%) was high, and the required competency was creative capacity (50.6%). Therefore, it is necessary to develop a curriculum related to the fourth industrial revolution and apply teaching methods that can increase the awareness and preparation of health college students in the era of the fourth industrial revolution.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.1-8
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• 2021

This study developed low-cost and high-efficiency pavement condition monitoring technology to produce the key information required for pavement management. A noise and artificial intelligence-based monitoring system was devised to compensate for the shortcomings of existing high-end equipment that relies on visual information and high-end sensors. From idea establishment to system development, functional definition, information flow, architecture design, and finally, on-site field evaluations were carried out. As a result, confidence in the high level of artificial intelligence evaluation was secured. In addition, hardware and software elements and well-organized guidelines on system utilization were developed. The on-site evaluation process confirmed that non-experts could easily and quickly investigate and visualized the data. The evaluation results could support the management works of road managers. Furthermore, it could improve the completeness of the technologies, such as prior discriminating techniques for external conditions that are not considered in AI learning, system simplification, and variable speed response techniques. This paper presents a new paradigm for pavement monitoring technology that has lasted since the 1960s.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.944-953
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• 2020

Object detection plays a crucial role in a self-driving system. With the advances of image recognition based on deep convolutional neural networks, researches on object detection have been actively explored. In this paper, we proposed a lightweight model of the mask R-CNN, which has been most widely used for object detection, to efficiently predict location and shape of various objects on the road environment. Furthermore, feature maps are adaptively re-calibrated to improve the detection performance by applying an attention module to the neural network layer that plays different roles within the mask R-CNN. Various experimental results for real driving scenes demonstrate that the proposed method is able to maintain the high detection performance with significantly reduced network parameters.

• The Journal of the Acoustical Society of Korea
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• v.40 no.1
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• pp.18-30
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• 2021

The ultimate goal of the present study is to establish the acoustical performance standards of classroom for the elderly who are incomplete hearing people. As a pilot survey, the present study was conducted to investigate the listening environment and the actual condition of speech perception performance of education facilities for elderly, Acoustic performances of two education facilities for elderly in Cheongju were measured and questionnaire survey was done to elderly people. Also, speech intelligibility tests were undertaken by Consonant Vowel Consonant (CVC) and Phonetically Balanced Words (PBW) methods. The questionnaire survey showed that the elderly were satisfied with the listening environment of the educational facilities in general. Also, it was found that acoustical performances satisfy with the acoustic criteria of general classrooms in Korea. However, the results of the speech intelligibility test showed that the scores of elderly were significantly lower than twenties with normal hearing. It was also revealed that the scores are reduced as the age increases. Thus, it was concluded that the acoustical performance standards of educational facilities for the normal hearing were not suitable for educational facilities for the elderly.

• The Journal of Society for e-Business Studies
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• v.26 no.2
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• pp.19-43
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• 2021

The prediction of box office performance in performing arts institutions is an important issue in the performing arts industry and institutions. For this, traditional prediction methodology and data mining methodology using standardized data such as cast members, performance venues, and ticket prices have been proposed. However, although it is evident that audiences tend to seek out their intentions by the performance guide poster, few attempts were made to predict box office performance by analyzing poster images. Hence, the purpose of this study is to propose a deep learning application method that can predict box office success through performance-related poster images. Prediction was performed using deep learning algorithms such as Pure CNN, VGG-16, Inception-v3, and ResNet50 using poster images published on the KOPIS as learning data set. In addition, an ensemble with traditional regression analysis methodology was also attempted. As a result, it showed high discrimination performance exceeding 85% of box office prediction accuracy. This study is the first attempt to predict box office success using image data in the performing arts field, and the method proposed in this study can be applied to the areas of poster-based advertisements such as institutional promotions and corporate product advertisements.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.531-540
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• 2021

As cyber attacks and crimes increase exponentially and hacking attacks become more intelligent and advanced, hacking attack methods and routes are evolving unpredictably and in real time. In order to reinforce the enemy's responsiveness, this study aims to propose a method for developing an artificial intelligence-based security control platform by building a next-generation security system using artificial intelligence to respond by self-learning, monitoring abnormal signs and blocking attacks.The artificial intelligence-based security control platform should be developed as the basis for data collection, data analysis, next-generation security system operation, and security system management. Big data base and control system, data collection step through external threat information, data analysis step of pre-processing and formalizing the collected data to perform positive/false detection and abnormal behavior analysis through deep learning-based algorithm, and analyzed data Through the operation of a security system of prevention, control, response, analysis, and organic circulation structure, the next generation security system to increase the scope and speed of handling new threats and to reinforce the identification of normal and abnormal behaviors, and management of the security threat response system, Harmful IP management, detection policy management, security business legal system management. Through this, we are trying to find a way to comprehensively analyze vast amounts of data and to respond preemptively in a short time.