• Journal of Digital Convergence
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• v.20 no.2
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• pp.325-338
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• 2022

This study was conducted to explore the acceptance behavior of undergraduate students toward telemedicine, which is temporarily allowed in the COVID-19. We applied social cognitive theory and technology acceptance model in order to reflect the convergence characteristics between medical service and digital technology of telemedicine. Based on these theoretical backgrounds, we investigated perception toward telemedicine and determinants of intention to accept telemedicine. To examine the research model and hypothesis, an online survey was conducted for college students who have not used telemedicine from September 8 to 10, 2021. A total of 184 data were collected, and multiple regression analysis was conducted using the SPSS 28.0 program. The results showed that health technology self-efficacy, usefulness and convenience benefits, social norm, and trust in telemedicine providers had positive effects on intention to accept telemedicine. This study is meaningful in that it selected undergraduate students, who are digital natives, as new targets for telemedicine, and presented the basic direction of strategies to target them.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.235-243
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• 2023

In this paper, we developed a problem-solving based safety education program using physical computing for middle school students and applied it to verify the impact on self-efficacy and interest. The safety education program developed in this study includes four stages of the creative problem-solving model: problem identification, planning, implementation, and evaluation, and learning activities using Arduino, a physical computing tool. After implementing the education program with 77 third-year middle school students, both self-efficacy and interest of middle school students increased significantly. Based on the research results, the effectiveness of the safety education program that used physical computing and problem-solving steps was confirmed, and practical implications were presented to promote the activation of physical computing education in the school field.

• Korean Journal of Weed Science
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• v.16 no.2
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• pp.100-107
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• 1996

Pre- and post-emergent control of Aneilema keisak was investigated in no-till paddy fields. In addition, a pot trial was conducted to determine use rates of the experimental post-emergent herbicide LGC40863. For pre-emergent control, butachlor(1,800g ai/ha), pretilachlor(600g ai/ha), pretilachlor plus pyrazosulfuron(300+18g ai/ha, respectively), thiobencarb plus bensulfuron(2,100+51g ai/ha, respectively), and molinate plus pyrazosulfuron(1,500+21g ai/ha, repectively) were treated at 20 days before seeding. Among the herbicides, molinate plus pyrazosulfuron was the least effective (23% control), while all other herbicides provided excellent(>95%) control of A. keisak. None of these herbicides caused rice phytotoxicity. However, rice yield in the plot treated with molinate plus pyrazosulfuron decreased about 50% due to poor A. keisak control. LGC40863 controlled A. keisak completely, by foliar application, across wide growth stages from the 5- to 15-leaf at 50g ai/ha in pot tests. In the field, treatment of LGC40863(30 to 50g ai/ha) provided >95% control of A. keisak when treated either at 15 days after transplanting or at non-productive tillering stage. Efficacy of 2,4-D and bentazon was insufficient when treated at non-productive tillering stage. These results suggest that, in no-till paddy fields, A. keisak is controlled by pre-emergent application of butachlor, pretilachlor, pretilachlor plus pyrazosulfuron, or thiobencarb plus bensulfuron, and by post-emergent application of LGC40863.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.139-149
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• 2022

The purpose of this study is to develop and analyze the effects of an educational program that can cultivate artificial intelligence(AI) convergence education competency for future education and enhance students' understanding of pre-service teachers. For this end, an AI convergence education program using Machine Learning for Kids and Scratch 3 was developed for 15 weeks under the theme of classifying the state of matter. The developed program were treated by K University pre-service teachers who participated voluntarily. As a result, pre-service teachers were able to metaphorically understand the learning process of students through understanding of machine learning training process. In addition, the pre-post t-test result of AI teaching efficacy showed a statistically significant improvement with t=-7.137 (p<.000). Therefore, it is suggested that the AI convergence education program developed in this study can help to increase the understanding of the pre-service teacher's students in an indirect way other than practice teaching, and can contribute to foster AI education competency.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.34 no.3
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• pp.202-213
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• 2024

Introduction: Acknowledging the global issue of diseases potentially caused by overwork, this study aims to develop an AI model to help workers understand the connection between cerebrocardiovascular diseases and their work environment. Materials and methods: The model was trained using medical and legal expertise along with data from the 2021 occupational disease adjudication certificate by the Industrial Accident Compensation Insurance and Prevention Service. The Polyglot-ko-5.8B model, which is effective for processing Korean, was utilized. Model performance was evaluated through accuracy, precision, sensitivity, and F1-score metrics. Results: The model trained on a comprehensive dataset, including expert knowledge and actual case data, outperformed the others with respective accuracy, precision, sensitivity, and F1-scores of 0.91, 0.89, 0.84, and 0.87. However, it still had limitations in responding to certain scenarios. Discussion: The comprehensive model proved most effective in diagnosing work-related cerebrocardiovascular diseases, highlighting the significance of integrating actual case data in AI model development. Despite its efficacy, the model showed limitations in handling diverse cases and offering health management solutions. Conclusion: The study succeeded in creating an AI model to discern the link between work factors and cerebrocardiovascular diseases, showcasing the highest efficacy with the comprehensively trained model. Future enhancements towards a template-based approach and the development of a user-friendly chatbot webUI for workers are recommended to address the model's current limitations.

• Korean Journal of Weed Science
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• v.18 no.4
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• pp.304-313
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• 1998

Foliar retention of pyribenzoxim (1% EC) was measured using the fluorescent dye rhodamine B, and related to efficacy and phytotoxicity to barnyardgarss (Echinochloa crusgalli) and rice (Oryza sativa cv. Chucheong), respectively. Effects of nozzle types (8002E flat-fan and disk-type), addition of adjuvant, variation of herbicide concentration or spray volume were compared. In barnyardgrass, foliar retention of pyribenzoxim at a recommended condition (application rate : 30g ai/ha, spray volume : 1000 L/ha) was 2.3 to 2.7 or 1.4 to $1.5{\mu}g$ ai/g fresh foliage when sprayed using the disk-type nozzle with or without adjuvant, respectively, and 0.6 to 0.7 or 0.3 to $0.5{\mu}g$ ai/g fresh foliage when sprayed using the flat-fan nozzle with or without adjuvant, respectively. The slope of increase in foliar retention was 1.0 to 1.8 when application rates increased from 10 to 60g ai/ha at 1000 L/ha, while that was 1.6 to 2.4 when spray volume increased from 330 to 2000 L/ha at $30{\mu}g$ ai/L concentration. Foliar retention of pyribenzoxim had a close relationship with herbicidal activity; regardless of spray conditions, retention to provide 90% control was about $0.8{\mu}g$ ai/g fresh foliage, and below this retention amount, efficacy decreased almost linearly. In rice, foliar retention at the recommended condition was 1.9 to 2.3 or 1.2 to $1.3{\mu}g$ ai/g fresh foliage when sprayed using the disk-type nozzle with or without adjuvant, respectively, and 0.6 to 0.9 or $0.3{\mu}g$ ai/g fresh foliage when sprayed using the flat-fan nozzle with or without adjuvant, respectively. The slope of increase in foliar retention was 1.0 to 2.8 when application rates increased from 30 to 120g ai/ha at 1000 L/ha, while that was 1.3 to 4.4 when spray volume increased from 1000 to 4000 L/ha at $30{\mu}g$ ai/L concentration. Despite the great difference in retention, rice phytotoxicity was not observed in any of these spray conditions, suggesting retention differences within 4-fold increase of application rate or spray volume do not affect rice safety. When pyribenzoxim 1EC was sprayed in tank-mix with several other commercial pesticide formulations, its retention to rice foliage tended to increase by 30 to 50%.

• Advanced Industrial SCIence
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• v.1 no.2
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• pp.17-23
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• 2022

The purpose of this study is to analyze the health factors of the elderly according to the region by analyzing the physical fitness and fall efficacy of the female elderly living in rural and urban areas. The subjects of this study consisted of 98 female elderly people living in rural and urban areas who exercise health and cheerleading at least twice a week at the regional center(REG; n=46, 77.53±6.37 yrs, 151.81±5.26 cm, 60.00±9.42 kg, UEG; n=53, 73.57±2.70 yrs, 154.07±3.52 cm, 57.37±2.06 kg). Physical strength was measured for muscular endurance, cardiopulmonary endurance, and flexibility. Falling efficacy was measured using 10 items of Fall Efficacy Scale developed by Tinetti et al. Significant differences in flexibility and fall efficacy were found in urban older adults (p<.01, p<.05). There was no significant difference in muscular endurance and cardiac endurance (p>.05). In the future, studies to improve the imbalance of health factors of the elderly in the region should be continuously conducted.

• Convergence Security Journal
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• v.23 no.2
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• pp.85-94
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• 2023

In the face of the serious security situation with the increasing threat from North Korea, Korean Army is pursuing a reduction in troops through the performance improvement project of the GOP science-based border security system, which utilizes advanced technology. In order for the GOP science-based border security system to be an effective alternative to the decrease in military resources due to the population decline, it must guarantee a high detection and identification rate and minimize troop intervention by dramatically improving the false detection rate. Recently introduced in Korean Army, the GOP science-based border security system is known to ensure a relatively high detection and identification rate in good weather conditions, but its performance in harsh weather conditions such as rain and fog is somewhat lacking. As an alternative to overcoming this, a radar-based border security system that can detect objects even in bad weather has been proposed. This paper proves the effectiveness of the AI-based scientific border security system based on radar that is being currently tested at the 00th Division through the 2021 Rapid Acquisition Program, and suggests the direction of development for the GOP scientific border security system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.133-139
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• 2024

In response to the challenges posed by a super-aged society, local authorities are conducting educational programs on smartphone usage tailored for the elderly. However, obstacles such as the limitations of one-to-many education and suboptimal learning outcomes for the elderly have hindered the efficacy of smartphone education. This study suggests an educational service intended for direct application in offline settings, considering the identified problems. Through the utilization of generative AI, the proposed app identifies specific challenges encountered by users during actual smartphone use, offering personalized exercises to facilitate customized and repetitive learning experiences for individual users. When integrated with existing local government education initiatives, this app is anticipated to enhance the efficiency of smartphone education by providing personalized, one-on-one training that is efficient in terms of time and content.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.64-72
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• 2024

In recent times cyber attackers can use Artificial Intelligence (AI) to boost the sophistication and scope of attacks. On the defense side, AI is used to enhance defense plans, to boost the robustness, flexibility, and efficiency of defense systems, which means adapting to environmental changes to reduce impacts. With increased developments in the field of information and communication technologies, various exploits occur as a danger sign to cyber security and these exploitations are changing rapidly. Cyber criminals use new, sophisticated tactics to boost their attack speed and size. Consequently, there is a need for more flexible, adaptable and strong cyber defense systems that can identify a wide range of threats in real-time. In recent years, the adoption of AI approaches has increased and maintained a vital role in the detection and prevention of cyber threats. In this paper, an Ensemble Deep Restricted Boltzmann Machine (EDRBM) is developed for the classification of cybersecurity threats in case of a large-scale network environment. The EDRBM acts as a classification model that enables the classification of malicious flowsets from the largescale network. The simulation is conducted to test the efficacy of the proposed EDRBM under various malware attacks. The simulation results show that the proposed method achieves higher classification rate in classifying the malware in the flowsets i.e., malicious flowsets than other methods.