• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.345-351
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• 2020

Administrative agencies today are paying keen attention to big data analysis to improve their policy responsiveness. Of all the big data, news articles can be used to understand public opinion regarding policy and policy issues. The amount of news output has increased rapidly because of the emergence of new online media outlets, which calls for the use of automated bots or automatic document classification tools. There are, however, limits to the automatic collection of news articles related to specific agencies or departments based on the existing news article categories and keyword search queries. Thus, this paper proposes a method to process articles using classification glossaries that take into account each agency's different work features. To this end, classification glossaries were developed by extracting the work features of different departments using Word2Vec and topic modeling techniques from news articles related to different agencies. As a result, the automatic classification of newspaper articles for each department yielded approximately 71% accuracy. This study is meaningful in making academic and practical contributions because it presents a method of extracting the work features for each department, and it is an unsupervised learning-based automatic classification method for automatically classifying news articles relevant to each agency.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.391-401
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• 2022

As deep learning-based object detection and recognition research have been developed recently, the scope of application to industry and real life is expanding. But deep learning-based systems in the construction system are still much less studied. Calculating materials in the construction system is still manual, so it is a reality that transactions of wrong volumn calculation are generated due to a lot of time required and difficulty in accurate accumulation. A fast and accurate automatic drawing recognition system is required to solve this problem. Therefore, we propose an AI-based automatic drawing recognition accumulation system that detects and recognizes steel materials in construction drawings. To accurately detect steel materials in construction drawings, we propose data augmentation techniques and spatial attention modules for improving small object detection performance based on YOLOv4. The detected steel material area is recognized by text, and the number of steel materials is integrated based on the predicted characters. Experimental results show that the proposed method increases the accuracy and precision by 1.8% and 16%, respectively, compared with the conventional YOLOv4. As for the proposed method, Precision performance was 0.938. The recall was 1. Average Precision AP_0.5 was 99.4% and AP_0.5:0.95 was 67%. Accuracy for character recognition obtained 99.9.% by configuring and learning a suitable dataset that contains fonts used in construction drawings compared to the 75.6% using the existing dataset. The average time required per image was 0.013 seconds in the detection, 0.65 seconds in character recognition, and 0.16 seconds in the accumulation, resulting in 0.84 seconds.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.318-331
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• 2022

With the recent development of hardware computing devices and software based frameworks, machine tasks using deep learning networks are expected to be utilized in various industrial fields and personal IoT devices. However, in order to overcome the limitations of high cost device for utilizing the deep learning network and that the user may not receive the results requested when only the machine task results are transmitted from the server, Collaborative Intelligence (CI) proposed the transmission of feature maps as a solution. In this paper, an efficient compression method for feature maps with vast data sizes to support the CI paradigm was analyzed and presented through experiments. This method increases redundancy by applying feature map reordering to improve compression efficiency in traditional video codecs, and proposes a feature map method that improves compression efficiency and maintains the performance of machine tasks by simultaneously utilizing image compression format and video compression format. As a result of the experiment, the proposed method shows 14.29% gain in BD-rate of BPP and mAP compared to the feature compression anchor of MPEG-VCM.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.1
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• pp.167-188
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• 2023

This study reviewed the current status of low-experienced teacher librarians with less than 5 years and attempted to identify their educational needs through IPA analysis, Borich Priority Formula, and The Locus for Focus Model. A survey was conducted on low-experienced teacher librarians with less than 5 years of experience to analyze their process in the pre-service teacher training and experiences before an appointment and to identify teacher librarians instructional expertise. The results of the analysis of the study are as follows. First, there was a statistically significant difference between the importance and performance in all areas of instructional expertise of low-experienced teacher librarians. Second, 'reading education-practice progress' was recognized as a 'Keep up good work' with high importance and satisfaction, and 'library-based instruction planning, progress evaluation', 'information literacy-curriculum design', and 'digital and media literacy education-progress and evaluation' were recognized as areas of 'Concentrate here' through IPA analysis. Third, In the Borich Priority Formula, 'teaching-learning evaluation', 'teaching-learning progress', and 'teaching-learning plan' in the Library based instruction area showed the highest educational needs. Fourth, the library-based instruction was shown to the high discrepancy/high importancy area as same as the Borich Proity Formula. The results of this study can provide implications for improving the instructional expertise of teacher librarians.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.936-939
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• 2022

As air combat system technologies developed in recent years, the development of air defense systems is required. In the operating concept of the anti-aircraft defense system, selecting an appropriate armament for the target is one of the system's capabilities in efficiently responding to threats using limited anti-aircraft power. Much of the flying threat identification relies on the operator's visual identification. However, there are many limitations in visually discriminating a flying object maneuvering high speed from a distance. In addition, as the demand for unmanned and intelligent weapon systems on the modern battlefield increases, it is essential to develop a technology that automatically identifies and classifies the aircraft instead of the operator's visual identification. Although some examples of weapon system identification with deep learning-based models by collecting video data for tanks and warships have been presented, aerial vehicle identification is still lacking. Therefore, in this paper, we present a model for classifying fighters, helicopters, and drones using a convolutional neural network model and analyze the performance of the presented model.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.105-112
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• 2022

Innovation is considered as an implemented innovation in education - in the content, methods, techniques and forms of educational activity and personality education (methods, technologies), in the content and forms of organizing the management of the educational system, as well as in the organizational structure of educational institutions, in the means of training and education and in approaches to social services in education, distance and multimedia learning, which significantly increases the quality, efficiency and effectiveness of the educational process. The classification of currently known pedagogical technologies that are most often used in practice is shown. The basis of the innovative activity of a modern teacher is the formation of an innovative program-methodical complex in the discipline. Along with programmatic and content provision of disciplines, the use of informational tools and their didactic properties comes first. It combines technical capabilities - computer and video technology with live communication between the lecturer and the audience. In pedagogical innovation, the principles reflecting specific laws and regularities of the implementation of innovative processes are singled out. All principles are elements of a complex system of organization and management of innovative activities in the field of education and training. They closely interact with each other, which enhances the effect of each of them due to the synergistic effect. To improve innovative activities in the training of students, today computer technologies are widely used in pedagogy as a science, as well as directly in the practice of the pedagogical process. They have gained the most popularity in such activities as distance learning, online learning, assistance in the education management system, development of programs and virtual textbooks in various subjects, searching for information on the network for the educational process, computer testing of students' knowledge, creation of electronic libraries, formation of a unified scientific electronic environment, publication of virtual magazines and newspapers on pedagogical topics, teleconferences, expansion of international cooperation in the field of Internet education. The article considers computer technologies as the main building material for the entire society. In the modern world, there is a need to prepare a person for life in a multimedia environment. This process should be started as early as possible, because the child's contact with the media is present almost from the moment of his birth.

• 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.4-7
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• 2022

Recently, multi-view depth estimation methods using deep learning network for the 3D scene reconstruction have gained lots of attention. Multi-view video contents have various characteristics according to their camera composition, environment, and setting. It is important to understand these characteristics and apply the proper depth estimation methods for high-quality 3D reconstruction tasks. The camera setting represents the physical distance which is called baseline, between each camera viewpoint. Our proposed methods focus on deciding the appropriate depth estimation methodologies according to the characteristics of multi-view video contents. Some limitations were found from the empirical results when the existing multi-view depth estimation methods were applied to a divergent or large baseline dataset. Therefore, we verified the necessity of obtaining the proper number of source views and the application of the source view selection algorithm suitable for each dataset's capturing environment. In conclusion, when implementing a deep learning-based depth estimation network for 3D scene reconstruction, the results of this study can be used as a guideline for finding adaptive depth estimation methods.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.117-132
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• 2022

Human Human species run into a blind alley due to abnormal weather and climates everywhere in global village. Human beings are helpless against the nature and might begin to learn humbleness just now. However, humans cannot attribute current abnormal weather and climates to only natural phenomenon because we have never been affectionate to global environments sufficiently up to now that results in running into this blind alley. At this point, the only thing that humans can do is to love and care for the earth more. ESG is an emerging topic to cope with this issue and practice of ESG will be the pending mission for the next generation. In this research, 'active participatory learning program for ESG practice' is designed by 'connecting ESG with Entrepreneurship' through over 20 years of experienced current teachers in elementary and junior·senior high school, professors in university and field experts in education. Analysis of learning effectiveness before and after the implementation of education program showed meaningful result in elementary and junior·senior high school. Thus, I would like to suggest a proposal based on concerns about 'What should we do to overcome the global crisis?" by paying attention to ESG from elementary school.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.21-30
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• 2024

As the self-driving car market continues to grow, the need for charging infrastructure is growing. However, in the case of a wireless charging system, stability issues are being raised because it requires a large amount of power compared with conventional wired charging. SAE J2954 is a standard for building autonomous vehicle wireless charging infrastructure, and the standard defines a communication method between a vehicle and a power transmission system. SAE J2954 recommends using physical media such as Wi-Fi, Bluetooth, and UWB as a wireless charging communication method for autonomous vehicles to enable communication between the vehicle and the charging pad. In particular, UWB is a suitable solution for indoor and outdoor charging environments because it exhibits robust communication capabilities in indoor environments and is not sensitive to interference. In this standard, the process for building a wireless power transmission system is divided into several stages from the start to the completion of charging. In this study, UWB technology is used as a means of fine alignment, a process in the wireless power transmission system. To determine the applicability to an actual autonomous vehicle wireless power transmission system, experiments were conducted based on distance, and the distance information was collected from UWB. To improve the accuracy of the distance data obtained from UWB, we propose a Single Model and Multi Model that apply machine learning and deep learning techniques to the collected data through a three-step preprocessing process.