• Journal of information and communication convergence engineering
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• v.9 no.1
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• pp.1-5
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• 2011

Everyday, technology is evolved in many different disciplines. Computer and smart devices revolution take part of the evolved technology that continuously promising new features. Moreover, social networks services recently become widely popular, which most people in the world become a social-network-fond. In addition to the revolution of the evolved technology and social networks services, ubiquitousness is taking significant part in our daily lives. Although, there are many e-learning systems already existed, which use Internet technology along with a Web technology to provide education in various ways, in despite of that, there is no such existing system exploits the usefulness of smart devices along with the legacy of the online social networks besides the power of the ubiquitous computing technology. Therefore, we propose a smart device application, which fills the gap that has been missing in the recent contemporary era. It is an application that runs on smart devices particularly Smartphone devices; we call our system “Smart Device based Social E-learning System(SDES)”. We have preliminary implemented our system on Android OS. In this paper, we intentionally propose the system in order to ease the way people learn, to provide interactive accessibility in our system, and to utilize the advanced technology more wisely.

• Journal of The Korean Association For Science Education
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• v.35 no.5
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• pp.829-840
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• 2015

In this study, we analyzed the teaching practices of science teachers using smart technologies and investigated their difficulties in implementing smart education and their educational needs. We also studied their opinions on the application of smart technologies in science education. The participants were seven science teachers who participated in the smart education study group of a science teacher association in Seoul. We elaborated on the characteristics of smart education in science education during comprehensive analyses of instructional materials used in science classes and the initial interviews. We then analyzed the second interviews by categorizing their responses inductively. All the science teachers used the 'instant access as needed', but their interactions, simply answering students' questions, were found to be at a low level. They did not effectively use the 'collaborative interaction with SNS or wiki-based service' for the support for interactive learning. While most collected learning results of their students and provided feedback in the aspect of 'individualization according to leaner level', they were not aware of 'context, situation, and location of learners' in smart education. While all the teachers extended learning opportunities by using learning resources widely in smart education, most were not aware of 'support for self-directed learning'. Most teachers believed that smart education should be developed to help students learn interactively and in a self-directed manner. They also provided many opinions on teacher training programs and environmental support for smart education. Based on the results, some considerations for implementing smart education in science instructions effectively are discussed.

• Journal of Service Research and Studies
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• v.11 no.4
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• pp.67-76
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• 2021

The importance of smart learning is increasing as the speed of development of non-face-to-face services increases due to the influence of COVID-19. This study is the user satisfaction evaluation model that utilizes the causal relationship between variables used for evaluation, focusing on the usability evaluation results of the learning disability intervention service (www.basic-edu.net) according to the need to evaluate the use satisfaction of the smart learning system. To this end, theoretical studies were conducted on smart learning and learning disability intervention services, www.basic-edu.net, usability evaluation of learning disability intervention systems, and use satisfaction evaluation models. And based on the results, a hypothesis was presented on the user satisfaction evaluation model of the smart learning system. The experimental method allowed 40 students and parents across the country to use the www.basic-edu.net service and was evaluated for its usability. In addition, using this data, the hypothesis was verified using regression analysis based on four variables: ease of use, interest, self-learning, and satisfaction with use. As a result of the hypothesis verification, it was found that the causal relationship of all hypotheses from H1 to H4 was significant.

• Journal of the Korean Society of Safety
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• v.35 no.2
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• pp.94-99
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• 2020

Bikers can be subjected to injuries from unexpected accidents even if they wear basic helmets. A properly designed airbag can efficiently protect the critical areas of the human body. This study introduces a wearable smart airbag system using machine learning techniques to protect human neck and shoulders. When a bicycle accident happens, a microprocessor analyzes the biker's motion data to recognize if it is a critical accident by comparing with accident classification models. These models are trained by a variety of possible accidents through machine learning techniques, like k-means and SVM methods. When the microprocessor decides it is a critical accident, it issues an actuation signal for the gas inflater to inflate the airbag. A protype of the wearable smart airbag with the machine learning techniques is developed and its performance is tested using a human dummy mounted on a moving cart.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.1
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• pp.55-67
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• 2015

This study examines innovation resistance to smart learning, an emerging innovative technology for startups and corporate ventures in the education market. The study explores whether the relative advantage, compatibility and complexity of an innovation, attitudes toward existing learning method(s), and perceived self-efficacy significantly affect innovation resistance. Additionally, the effects of such innovation resistance on future use and the moderating effect according to demographic characteristics are examined. The results of the analysis using a structural equation model showed that all the factors considered (except relative advantage) affects innovation resistance, innovation resistance significantly affects intention to use.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.155-165
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• 2020

In this paper, we propose a smart thermostat temperature set-point control method based on machine learning and rule engine, which controls thermostat's temperature set-point so that it can achieve energy savings as much as possible without sacrifice of occupants' comfort while users' preference usage pattern is respected. First, the proposed method periodically mines data about how user likes for heating (winter)/cooling (summer) his or her home by learning his or her usage pattern of setting temperature set-point of the thermostat during the past several weeks. Then, from this learning, the proposed method establishes a weekly schedule about temperature setting. Next, by referring to thermal comfort chart by ASHRAE, it makes rules about how to adjust temperature set-points as much as low (winter) or high (summer) while the newly adjusted temperature set-point satisfies thermal comfort zone for predicted humidity. In order to make rules work on time or events, we adopt rule engine so that it can achieve energy savings properly without sacrifice of occupants' comfort. Through experiments, it is shown that the proposed smart thermostat temperature set-point control method can achieve better energy savings while keeping human comfort compared to other conventional thermostat.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.215-219
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• 2021

In this paper, we propose a deep reinforcement learning algorithm-based bi-directional electricity negotiation scheme that adjusts and propose the price they want to exchange for negotiation over smart building and utility grid. By employing a deep Q network algorithm, which is a kind of deep reinforcement learning algorithm, the proposed scheme adjusts the price proposal of smart building and utility grid. From the simulation results, it can be verified that consensus on electricity price negotiation requires average of 43.78 negotiation process. The negotiation process under simulation settings and scenario can also be confirmed through the simulation results.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.143-150
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• 2024

With the development of AI, multimedia tools in education offer personalized learning environments, which foster individual competencies. This study aims to examine the effectiveness of smart education as perceived by learners through a case study of university writing classes utilizing multimedia learning tools, and to explore potential applications. To achieve this, a writing course incorporating various multimedia tools to promote interaction was designed and implemented over the course of one semester, targeting 42 university students. Through the semester, student reactions and survey results were analyzed to investigate the effects and satisfaction levels regarding the use of multimedia learning tools in writing instruction as perceived by students. The analysis revealed that multimedia-assisted writing classes effectively fostered learners' autonomy by focusing on individual needs, while also promoting interaction and encouraging spontaneous participation. Students reported recognizing the presence of diverse perspectives by comparing and communicating about each other's writing, leading to an expansion of their own thinking. In using ChatGPT, it was found that students attempted to refine their questions until they obtained the desired answers. They reported that this process deepened their understanding of the essence of the questions. These benefits led to results of high levels of students' active class engagement and satisfaction. This study contributes foundational and empirical data regarding the effectiveness and potential applications of learner-centered smart education as part of fourth industrial revolution integration research.

• 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.