• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• 한국노년학
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• v.30 no.4
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• pp.1129-1143
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• 2010

The purposes of this study were to investigate the major determinants influencing on health promoting behaviors(HPB) of the elderly living in Seoul. The conceptual framework of the study was Pender's health promoting model and the ecological perspectives. The study was conducted with 495 elderly persons whom 60 years old. For the analysis of data, descriptive statistics and hierarchical regression were used for the statistical analysis with SPSS program. The results were as following: 1) The mean score of the HPB was 3.11(SD=0.41). 2) Hierarchical regression analysis found that ModelIV accounted for 55.7% of the variance in HPB. 3) The Major determinants on HPB among the elderly persons were prior related perceived benefits of action, social support, perceived self-efficacy, community environment, perceived health status, education, and age. In conclusions, first, we should develop to various levels of educational and supportive programs for the HPB among the elderly persons. Second, we should examine more with environment, the accessibility to senior welfare agencies. Third, we should be organized the self-help groups for the elderly persons to improve health promoting behaviors. Fourth, the government should established more secure environment for the HPB, and find better solutions that are provided by various social welfare agencies connected with the coordination of the services in the local communities. Finally, we should develop professional education training programs of the HPB for the practitioners in the field of Gerontological Social Work.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.543-553
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• 2022

A collaborative robot(Cobot) is one of the production systems presented in the 4th industrial revolution and are systems that can maximize efficiency by combining the exquisite hand skills of workers and the ability of simple repetitive tasks of robots. Also, research on the development of an efficient interface method between the worker and the robot is continuously progressing along with the solution to the safety problem arising from the sharing of the workspace. In this study, a method for controlling the robot by recognizing the worker's hand signal was presented to enhance the convenience and concentration of the worker, and the safety of the worker was secured by introducing the concept of a safety zone. Various technologies such as robot control, PLC, image processing, machine learning, and ROS were used to implement this. In addition, the roles and interface methods of the proposed technologies were defined and presented for using educational media. Students can build and adjust the educational media system by linking the introduced various technologies. Therefore, there is an excellent advantage in recognizing the necessity of the technology required in the field and inducing in-depth learning about it. In addition, presenting a problem and then seeking a way to solve it on their own can lead to self-directed learning. Through this, students can learn key technologies of the 4th industrial revolution and improve their ability to solve various problems.

• The Journal of Korean Medical History
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• v.36 no.1
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• pp.1-9
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• 2023

To be an adjunct professor(gyeomgyosu) literally means to act as an instructor while also holding a different position. Adjunct professors were initially introduced under Confucianism. Gradually, technical offices also appointed adjunct professors using Confucian-educated bureaucrats for the purpose of educating lower-level technical officials and cadets. This paper examines the history of the civil service system related to adjunct professors through the Code of Laws, and examines those who have been appointed to the public office described in various documents. This paper argues that changes in the medical office's adjunct professor system reflect changes in the national medical talent training policy. The main basis of specific recognizing medical personnel is to decouple the appointment of Confucian scholars from that of full-time doctors. The replacement of the role of medical educators from Confucian scholars to full-time doctors was largely accomplished during the reign of King Jungjong(中宗) and was completed during the period of King Injo(仁祖). The time when Euiyakdongcham was created and the Office of Euiyakdongcham was established coincided with the period when the adjunct professor was disrupted in the medical office. However, this change in the adjunct professor system of medical authorities is in contrast to interpretation, which is a representative technical field. In the case of interpretation, Moonshin's sayeogwon position as adjunct professor was maintained even in the late Joseon Dynasty, and apart from this, there was a hanhagmunsin in Seungmunwon. Interpreter families had institutional arrangements that prevented them from making interpretation their own monopoly. Therefore, families of medical bureaucrats had more room for institutional growth than those of bureaucratic interpreters. Of course, these institutional devices did not prevent the growth of interpreting bureaucratic families in the late Joseon Dynasty. However, the situation in which medicine was accepted only as a kind of knowledge, not as an object of full-time work for sadaebue, would have been an opportunity to rise for those in technical jobs who were full-time medicine. As medicine became more differentiated and developed in the late Joseon Dynasty, medical knowledge and the knowledge about the medical profession became more important. The politicians could not avoid the use of a philosophically oriented system in which a confucian-educated bureaucrat equipped with only Confucian knowledge might replace a full-time doctor. Thus, the contradiction between the reality and the ideal of ignoring or denying reality was reproduced like other Confucian-centered societies. These contradictions have implications for us living in the modern age. Establishing the relationship between philosophy (or belief) and technology should not end with the superiority of one side or the other.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.1-11
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• 2023

Breast cancer is the disease that affects women the most worldwide. Due to the development of computer technology, the efficiency of machine learning has increased, and thus plays an important role in cancer detection and diagnosis. Deep learning is a field of machine learning technology based on an artificial neural network, and its performance has been rapidly improved in recent years, and its application range is expanding. In this paper, we propose a DNN-SVM hybrid model that combines the structure of a deep neural network (DNN) based on transfer learning and a support vector machine (SVM) for breast cancer classification. The transfer learning-based proposed model is effective for small training data, has a fast learning speed, and can improve model performance by combining all the advantages of a single model, that is, DNN and SVM. To evaluate the performance of the proposed DNN-SVM Hybrid model, the performance test results with WOBC and WDBC breast cancer data provided by the UCI machine learning repository showed that the proposed model is superior to single models such as logistic regression, DNN, and SVM, and ensemble models such as random forest in various performance measures.

• Journal of Venture Innovation
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• v.6 no.1
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• pp.59-73
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• 2023

In the era of the 4th Industrial Revolution, manufacturing is the implementation of smart factories through digital transformation, and refers to consumer-centered intelligent factories that combine next-generation digital new technologies and manufacturing technologies beyond the existing factory automation level. In order to successfully settle such a smart factory, it is necessary to train professionals. However, education for smart factories is difficult to have actual field mechanical facilities or overall production processes. Therefore, there is a need for a system that can visualize and control the flow and process of logistics at the actual production site. In this paper, the logistics flow of the actual site was implemented as a small FMS, a physical system, and the production process was implemented as a digital system. In real-time synchronization of the physical system and the digital system, the location of AGV and materials, and the process state can be monitored to see the flow of logistics and process processes at the actual manufacturing site. The developed digital twin system can be used as an effective educational system for training manpower in smart factories.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.299-316
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• 2023

With the importance and necessity of using AI in the field of education, this study aims to explore elementary school teachers' perceptions of using Artificial Intelligence (AI) in mathematics education. For this purpose, we conducted a survey using a 5-point Likert scale with 161 elementary school teachers and analyzed their perceptions of mathematics education with AI via four categories (i.e., Attitude of using AI, AI for teaching mathematics, AI for learning mathematics, and AI for assessing mathematics performance). As a result, elementary school teachers displayed positive perceptions of the usefulness of AI applications to teaching, learning, and assessment of mathematics. Specifically, they strongly agreed that AI could assist personalized teaching and learning, supplement prerequisite learning, and analyze the results of assessment. They also agreed that AI in mathematics education would not replace the teacher's role. The results of this study also showed that the teachers exhibited diverse perceptions ranging from negative to neutral to positive. The teachers reported that they were less confident and prepared to teach mathematics using AI, with significant differences in their perceptions depending on whether they enacted mathematics lessons with AI or received professional training courses related to AI. We discuss the implications for the role of teachers and pedagogical supports to effectively utilize AI in mathematics education.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.177-191
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• 2023

This study analyzes the types of linkages between major projects used by firms that attracted venture capital among firms that received government support in the field of SME startups. It identifies the types of linkages between support programs related to attracting venture investment and verifies the usefulness of integrated and cooperative support. The main findings of this study are as follows. First, Startup Success Packages, Startup Foundation Funds*, Youth Entrepreneurship Centers, and Training are the main programs used by startups and venture firms, and support-implementing agencies use these programs to provide support for each stage of growth. Second, the majority of startups and venture firms receiving policy support for job creation and manpower enhancement projects. Third, export-type growth companies receive continuous support from MSS, MOTIE, MSIT, and KIPO. Fourth, job creation programs drive the employment performance and creation of companies. Fifth, local government support projects tend to rely heavily on central government support programs. Sixth, growth companies in the startup and venture sector have a clear link to credit guarantee scheme by KIBO. These findings provide empirical evidence on the necessity and feasibility of integrated and collaborative support, and are expected to contribute to the direction of better support policies.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.137-142
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• 2024

Recently, deep learning technology has become those methods as de facto standards in the area of medical data representation. But, deep learning inherently requires a large amount of training data, which poses a challenge for its direct application in the medical field where acquiring large-scale data is not straightforward. Additionally, brain signal modalities also suffer from these problems owing to the high variability. Research has focused on designing deep neural network structures capable of effectively extracting spectro-spatio-temporal characteristics of brain signals, or employing self-supervised learning methods to pre-learn the neurophysiological features of brain signals. This paper analyzes methodologies used to handle small-scale data in emerging fields such as brain-computer interfaces and brain signal-based state prediction, presenting future directions for these technologies. At first, this paper examines deep neural network structures for representing brain signals, then analyzes self-supervised learning methodologies aimed at efficiently learning the characteristics of brain signals. Finally, the paper discusses key insights and future directions for deep learning-based brain signal analysis.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.73-82
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• 2024

In 2017, the United Nations reported that the population aged 60 and above was increasing more rapidly than all younger age groups worldwide, projecting that by 2050, the population aged 60 and above would constitute at least 25% of the global population, excluding Africa. The world is experiencing a decline in the rate of increase in the working-age population due to global aging, and the younger generation tends to avoid difficult and challenging occupations. Although theoretically, AI equipped with artificial intelligence can replace humans in all fields, in the realm of practical information security, human judgment and expertise are absolutely essential, especially in ethical considerations. Therefore, this paper proposes a method to retrain and reintegrate IT professionals aged 50 and above who are retiring or seeking career transitions, aiming to bring them back into the industry. For this research, surveys were conducted with 21 government/public agencies representing demand and 9 security monitoring companies representing supply. Survey results indicated that both demand (90%) and supply (78%) unanimously agreed on the absolute necessity of such measures. If the results of this research are applied in the field, it could lead to the strategic development of senior information security professionals, laying the foundation for a new market in the Korean information security industry amid the era of low birth rates and longevity.