• 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.

• 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.

• Korean Journal of Agricultural Science
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• v.12 no.2
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• pp.183-190
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• 1985

To determine anti-lodging effect of paclobutrazol and its influence on yield of rice, paclobutrazol was applied at 12 and 18g ai/10a to cv. Nakdongbyeo and cv. Seomjinbyeo 20, 15, and 10 days before heading under 12 and 20kg/10a nitrogen conditions. 1. Paclobutrazol reduced internode length of rice, especially internode N3. Higher suppression of stem of both cultivars and reduction of ear length in cv. Nakdongbyeo were observed when paclohutrazol was applied at 18g ai/10a 15 and 20 days before heading. 2. Paclobutrazol increased culm cross-section area, culm diameter, linear density and stem flexure resistance which affected anti-lodging efficacy. Although actual lodging was not occurred in field, less stem inclination was observed at paclobutrazol treated plots. 3. There were significant correlation between stem flexure resistance and culm cross-section area, culm diameter and linear density, and negative correlation between straw inclination angle and culm cross-section area and culm diameter. 4. Slight yield reduction was observed when paclobutrazol was applied at 18g ai/10a 20 days before heading. However, when paclobutrazol was applied at 12g ai/10a 10 and 15 days before heading, rice yield was not decreased compared to untreated control.

• Journal of Korean Library and Information Science Society
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• v.53 no.1
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• pp.231-263
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• 2022

The understanding and capability to utilize artificial intelligence (AI) incorporated technology has become a required basic skillset for the people living in today's information age, and various members of the university have also increasingly become aware of the need for AI education. Amidst such shifting societal demands, both domestic and international university libraries have recognized the users' need for educational content centered on AI, but a user-centered service that aims to provide personalized recommendations of digital AI educational content is yet to become available. It is critical while the demand for AI education amongst university students is progressively growing that university libraries acquire a clear understanding of user intention towards an AI educational content recommender system and the potential factors contributing to its success. This study intended to ascertain the factors affecting acceptance of such system, using the Extended Technology Acceptance Model with added variables - innovativeness, self-efficacy, social influence, system quality and task-technology fit - in addition to perceived usefulness, perceived ease of use, and intention to use. Quantitative research was conducted via online research surveys for university students, and quantitative research was conducted through written interviews of university librarians. Results show that all groups, regardless of gender, year, or major, have the intention to use the AI-related Educational Content Recommendation System, with the task suitability factor being the most dominant variant to affect use intention. University librarians have also expressed agreement about the necessity of the recommendation system, and presented budget and content quality issues as realistic restrictions of the aforementioned system.

• Journal of Service Research and Studies
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• v.13 no.4
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• pp.206-223
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• 2023

The introduction of Smart technologies such as Artificial Intelligence(AI) systems are have a powerful impact in a variety of industry fields. Some experts predict that smart technology will completely change people's daily life and work styles, causing technological innovation, productivity improvement, and discovery and emergence of new fields. On the one hand, this vision cannot ignore negative views and concerns. Despite many social debates about employment, such as job loss and rising unemployment, there have not been many studies based on employee experience that provide a fundamental solution to the conflict between AI and employment. Therefore, this study finds out the effects and related factors of AI concierge robots for hotel employees, focusing on the hotel industry, and how employees' perceptions of AI concierge robots affect user resistance and turnover intention. This study, conducted a questionnaire survey of 322 hotel employees who had experience working with AI concierge robots in China, and used SPSS and SmartPLS statistical analysis programs to draw conclusions. We found that hotel employees' perceptions of AI concierge robots were significantly related to user resistance and turnover intention, and this association was related to employee self-efficacy, perceived organizational support, quality of AI services and new tasks. In addition, it was found that the quality of AI concierge robots directly or indirectly had the greatest influence on user resistance and turnover intention. The findings of this study provide theoretical implications for academia and practical implications for industry practitioners.