• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Smart Media Journal
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• v.12 no.5
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• pp.65-72
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• 2023

Smart Farm means creating new value in various fields related to agriculture, including not only agricultural production but also distribution and consumption through the convergence of agriculture and ICT. In Korea, a rental smart farm is created to spread smart agriculture, and a smart farm big data platform is established to promote data collection and utilization. It is pushing for digital transformation of agricultural products distribution from production areas to consumption areas, such as expanding smart APCs, operating online exchanges, and digitizing wholesale market transaction information. As such, although agricultural data is generated according to characteristics from various sources, it is only used as a service using statistics and standardized data. This is because there are limitations due to distributed data collection from agriculture to production, distribution, and consumption, and it is difficult to collect and process various types of data from various sources. Therefore, in this paper, we analyze the current state of domestic agricultural data collection and sharing for digital agriculture and propose a data collection and linkage method for artificial intelligence services. And, using the proposed data, we propose a deep learning-based environmental factor recommendation method.

• Journal of Venture Innovation
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• v.5 no.4
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• pp.41-56
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• 2022

This study was conducted to empirically analyze the effect of various job characteristics on job satisfaction under the smart work environment, which has become a hot topic recently. To this end, job characteristics under the smart work environment were subdivided into job autonomy, job flexibility, and job efficiency. In addition, although these job characteristics had a direct effect on job satisfaction, the learning agility of employees was also considered to be important factors. Job autonomy, job flexibility, and job efficiency all had a significant positive (+) effect on job satisfaction. In the case of learning agility, it was found that there was a mediating effect in all paths. The results of this study had academic significance in that they empirically tested the relationship between job characteristics of smart work and job satisfaction according to the progress to contact free society..

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3333-3350
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• 2019

The smart jammer launches jamming attacks which degrade the transmission reliability. In this paper, smart jamming attacks based on the communication probability over different channels is considered, and an anti-jamming Q learning algorithm (AQLA) is developed to obtain anti-jamming knowledge for the local region. To accelerate the learning process across multiple regions, a multi-regional intelligent anti-jamming learning algorithm (MIALA) which utilizes transferred knowledge from neighboring regions is proposed. The MIALA algorithm is evaluated through simulations, and the results show that the it is capable of learning the jamming rules and effectively speed up the learning rate of the whole communication region when the jamming rules are similar in the neighboring regions.

• Smart Media Journal
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• v.13 no.4
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• pp.104-113
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• 2024

This study is a model study on the contents and methods of intercultural learning. Starting with a discussion of the intercultural learning model construct, it presents key contents important for intercultural learning and learning methods that can increase the effectiveness of intercultural learning. Also, we actually conducted the above learning program at the learning site and discussed the observations and results. It was a case study that allowed us to test the effectiveness of cultural intelligence theory, the latest theory that can improve intercultural competency. In addition, in order for the cultural intelligence theory to be effective in the learning process, it was found that the PBL method, which allows learners to solve problems on their own, rather than cramming education, is useful. Additionally, it was found that the ARCS model was also very effective in motivating and maintaining learners' continuous motivation. At this time, the instructor was also able to see that the effect increases when the role of catalyst becomes the main one.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.6
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• pp.25-32
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• 2012

In this paper, we proposed a development of M-learning smart-trainer content for improving the learning ability of military education. Learners of time and space constraints beyond quickly and accurately can learn with the goal, each subject by partial learning, and repetition, the whole learning quickly and easily by selecting efficiently to help you learn a m-Bizmaker with applications was designed. Experiment targets the military company of two, first aid courses were conducted for the evaluation. Traditional collective comparison group teaching methods, the proposed content, teaching methods applied in the experimental group were selected. The proposed learning applications using smart instructor for verification of learning, with which to compare, test subjects were compared with each of 49 subjects, the results p<.005 level, there was difference among the two groups. Therefore, the proposed application using a smart trainer after class proved that contribute to improving achievement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.463-470
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• 2015

The purpose of this thesis is to develop and implement a smart learning system based on problem-solving strategy for children with learning disabilities. The proposed system is developed to increase general study ability and problem-solving ability of children with learning disabilities. The proposed system has the following characteristics. First, the general study ability and problem-solving ability can be increased by adopting problem-solving strategy. Second, both smart application and SNS can be used in the proposed system. Third, study self-efficacy can be increased by adopting step-by-step learning. The following results are obtained after applying the proposed system to some children with learning disabilities. First, their general study ability is increased. Second, their problem-solving ability is increased. Third, confidence in self-efficacy, sense of accomplishment, and self-confidence in study are improved. In additions, utilization ability in information and information equipment is also increased.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.416-434
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• 2022

This study, for learners using online and offline tools, understood the structural relationship of user experience of smart learning app on continuous use intention through the technology acceptance model, and classified the learning type characteristics. In addition, based on the experience of using the smart learning app, we explored ways to improve the design of the user experience design for learning tools and contents. For this purpose, the usage perception of 84 middle and high school students of the developed smart learning learning app was investigated after using it for 2 months, and the data were analyzed using the PLS structural equation technique. The main results of this study are as follows. First, system and content user experience had a significant effect on perceived usability and perceived ease of use, and the effect on continued use intention through attitude was significant. Second, there was a significant difference in the effect of system user experience on perceived usefulness in multi-group comparative analysis and gender group. In the preferred learning group, it was the path from perceived ease of use and perceived usefulness to attitude and intention to continue using that showed a significant path difference. Third, as a result of classifying the most commonly used learning types by the multidimensional scale method, the types separated into low dimensions were found to be four types: offline sync type, online sync type, ubiquitous learning type, and self-direct learning type.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.429-431
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• 2011

공학의 기본 원리 학습을 위해서는 원리 그림이나 도표와 같은 콘텐츠의 사용이 필요하다. 하지만 시간과 공간의 제약으로 학습이 불충분하고, 편리성과 멀티미디어 콘텐츠를 갖추지 못하면 학습의 효과도 떨어지는 문제점이 있다. 최근 Smart Phone 사용자가 증가하고, Smart Phone을 이용한 교육프로그램을 제공하여 이동 중에도 공학의 기본 원리와 같은 학습이 필요한 시점이다. 본 논문에서는 열역학의 법칙 등 열역학에 관한 학습 콘텐츠를 Smart Phone에서 구현하고자 한다. 실제 Web 서버를 통하여 무선 인터넷 통신망에서 제공되는 열역학의 교육 공학적 도표, 애니메이션, 그림, 등을 Smart Phone에서의 설계하고 구현한다. 본 연구는 교육공학과 학습 프로그램 제공을 통해 모바일 원격교육 발전에 기여할 것이다.

• Journal of the Korea Convergence Society
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• v.8 no.1
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• pp.25-34
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• 2017

With the recent spread of smartphones and the introduction of web services, online users can access large-scale content regardless of time or place. However, users have had trouble finding the content they wanted among large-scale content. To solve this problem, user modeling and content recommendation system have been actively studied in various fields. However, in spite of active changes in senior groups according to the changes in information environment, research on user modeling and content recommendation system focused on senior groups are insufficient. In this paper, we propose a method of modeling smart senior based on their preference, and further develop a smart senior classification model using machine learning methods. As a result, we can not only grasp the preferences of smart seniors, but also develop a smart senior classification model, which is the foundation for the research of a recommendation system which will provide the activities and contents most suitable for senior groups.