• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.383-394
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• 2018

This study analyzed the latent profile for identifying the difference in the dependence on smartphone use among middle school students in the 1st grade using the Korean Children and Youth Panel Survey (KCYPS). From the result of this study, first the latent class was separated according to the type of dependence on smartphone use. Class 1 included the students (from fifth grade in elementary school) whose level of reliance on smartphone use was low. Class 2 was selected as the group whose level of reliance on smartphone was high. Secondly, in comparing class 2 to class 1, it was found that the students who have a high probability of being in class 1 were those whose fathers are high achievers, have high early self-esteem and less age attachment. Thirdly, the students in class 1 had a higher sense of neglect than those in class 2. Furthermore, the self-determination of the students in class 2 mediated the effect of neglect on the adolescents' dependence on smartphone use both directly and indirectly.

• Journal of the Korean Society of Radiology
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• v.11 no.4
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• pp.279-287
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• 2017

The purpose of this was to study and analyze smart learning the self directed learning, self efficacy, learning satisfaction about department of radiology in a college. For this study total students 74 in 2classes were surveyed at the end of semester. Compared to use smartphones one group and not use smartphones one group for study in a class. The research data was analyzed using SPSS also self directed learning, self learning efficacy, learning satisfaction analyzed t-test, general character was analyzed two group(one : Used smart learning other : not Used smart learning) ${\chi}^2-test$. First, Used smart learning group is more higher than not Used smart learning group in a self learning efficacy, self directed learning, learning satisfaction. Second, during the smart learning classes a students appeared a positive response. Suggest to change a paradigm in a radiology classes so we have to improve a teaching skills this solution recommend is two way communication. In conclusion, smart learning applied for classes of college is meaningful as a new teaching, which can be change gradually learning satisfaction by teaching methods.

• Journal of the Korean earth science society
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• v.41 no.2
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• pp.176-193
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• 2020

The purpose of this study was to explore the effects of science classes on positive science experiences and science learner identity, using smart devices in a future school: C middle school. We conducted a paired t test at the beginning and end of the first school year with first-grade students at the future school to investigate positive experiences with science (Shin et al., 2017). Additionally, first and second-grade students in future schools using smart devices wrote and drew their own depictions in science classes to explore science learner identity, based on a modified analytical framework (Luehmann, 2009). The results show that significant effects on science-related career aspirations, self-concepts, and academic emotions were produced by science classes using smart devices. Science classes using smart devices helped students improve their level of agency and activity, solve problems with immediate and sufficient feedback, and experience meaningful perceptions of the nature of science. On the other hand, if students were immature in terms of their use of smart devices, they felt pressured to participate in the classes. The results of this study can be used as a foundation for designing various classroom contexts for the use of smart devices.

• Journal of The Korean Association For Science Education
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• v.36 no.4
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• pp.519-526
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• 2016

The purpose of this study is to investigate the influences of small group learning using smart devices in science classes on students' achievement, learning motivation, attitude toward science lessons, and perception of small group learning using smart devices. Four 11th-grade classes (N=133) at a coed high school in Seoul were randomly assigned to a control group and a treatment group. The intervention of small group learning using smart devices emphasized collaborative writing on activity sheet. The students were taught about acid, base, and neutralization reaction for six class periods. After the instructions, an achievement test, the learning motivation test, the attitude toward science lessons test, and a questionnaire on the perception of small group learning using smart devices were administered. Two-way ANCOVA results revealed that there was a statistically significant interaction effect by their previous chemistry achievement in the achievement test scores. Only low-level students in small group learning using smart devices significantly improved their achievement probably by having the opportunities to get help from high-level students. The adjusted means of the treatment group were significantly higher than those of the control group in learning motivation and attitude toward science lessons. Students' perceptions of small group learning using smart devices tended to be positive. Educational implications of this study are discussed.

• Smart Structures and Systems
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• v.21 no.4
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• pp.435-448
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• 2018

This paper reports the development of a theoretically rigorous method for permanent way engineers to assess the condition of railway ballast under a concrete sleeper with the potential to be extended to a smart system for long-term health monitoring of railway ballast. Owing to the uncertainties induced by the problems of modeling error and measurement noise, the Bayesian approach was followed in the development. After the selection of the most plausible model class for describing the damage status of the rail-sleeper-ballast system, Bayesian model updating is adopted to calculate the posterior PDF of the ballast stiffness at various regions under the sleeper. An obvious drop in ballast stiffness at a region under the sleeper is an evidence of ballast damage. In model updating, the model that can minimize the discrepancy between the measured and model-predicted modal parameters can be considered as the most probable model for calculating the posterior PDF under the Bayesian framework. To address the problems of non-uniqueness and local minima in the model updating process, a two-stage hybrid optimization method was developed. The modified evolutionary algorithm was developed in the first stage to identify the important regions in the parameter space and resulting in a set of initial trials for deterministic optimization to locate all most probable models in the second stage. The proposed methodology was numerically and experimentally verified. Using the identified model, a series of comprehensive numerical case studies was carried out to investigate the effects of data quantity and quality on the results of ballast damage detection. Difficulties to be overcome before the proposed method can be extended to a long-term ballast monitoring system are discussed in the conclusion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.2
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• pp.357-372
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• 2012

Existing e-learning systems not only require complex admission processes but also do not give stepwise education methods according to individual learners' characteristic. These circumstances cause learners to lose educational interest so that their educational efficiency decreases. In particular, the present e-learning systems do not provide educational approaches suitable for infant and elementary children. Under this system, the e-learning education for children does not proceed completely without guardians. To solve this problem, we design and implement an English e-learning system for elementary children based on friendly and comfortable user interfaces. For children, the proposed system reflects their age and individual interesting per each e-learning stage. This system supports both the Web application platform and smart phone application platform for various client requirements. The proposed system manages 3 classes as English learning content. Learners can experience their own English e-learning course in each class, which is compiled by current educational ability. In addition to the general functions in e-learning system, the proposed system develops content buffering algorithm to reduce data traffic in server.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.219-223
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• 2021

This study aims to present a non-face-to-face teaching methodology for the theory and practical lessons of design majors, especially for the arts and sports field. It was conducted to improve the existing teaching models after the non-face-to-face online lectures which began with COVID-19. Various existing smart learning methods such as online classes, interactive classes, and flip learning were reviewed, and a method to efficiently manage practical skills by supplementing the shortcomings of each study was suggested. 4 stages of teaching development - setting a teaching method, teaching progress, evaluation, and follow-up management-were designated and applied to the class of design majors. The result showed that it is effective in terms of teaching method and progress; however, the limitations of non-face-to-face classes were found in the stages of evaluation and follow-up management. Therefore, it is expected that further research on evaluation and follow-up management, such as specific practical instruction methods is required to improve completion.

• Smart Media Journal
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• v.10 no.4
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• pp.45-54
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• 2021

The skin is the body's first line of defense against external infection. When a skin disease strikes, the skin's protective role is compromised, necessitating quick diagnosis and treatment. Recently, as artificial intelligence has advanced, research for technical applications has been done in a variety of sectors, including dermatology, to reduce the rate of misdiagnosis and obtain quick treatment using artificial intelligence. Although previous studies have diagnosed skin diseases with low incidence, this paper proposes a method to classify common illnesses such as warts and corns using a convolutional neural network. The data set used consists of 3 classes and 2,515 images, but there is a problem of lack of training data and class imbalance. We analyzed the performance using a deep metric loss function and a cross-entropy loss function to train the model. When comparing that in terms of accuracy, recall, F1 score, and accuracy, the former performed better.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1720-1728
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• 2022

The industrial 4.0 era has been opened with the development of artificial intelligence technology, and the realization of smart farms incorporating ICT technology is receiving great attention in the livestock industry. Among them, the quality management technology of livestock products and livestock operations incorporating computer vision-based artificial intelligence technology represent key technologies. However, the insufficient number of livestock image data for artificial intelligence model training and the severely unbalanced ratio of labels for recognizing a specific defective state are major obstacles to the related research and technology development. To overcome these problems, in this study, combining oversampling and adversarial case generation techniques is proposed as a method necessary to effectively utilizing small data labels for successful defect detection. In addition, experiments comparing performance and time cost of the applicable techniques were conducted. Through experiments, we confirm the validity of the proposed methods and draw utilization strategies from the study results.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.895-902
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• 2016

Smart phone distribution rate has been rising and it's security threat also has been rising. Especially Android smart phone reaches nearly 85% of domestic share. Since repackaging on android smart phone is relatively easy, the number of re-packaged malwares has shown steady increase. While many detection techniques have been proposed in order to prevent malwares, it is not easy to detect re-packaged malwares by static analysis and it is also difficult to operate dynamic analysis in android smart phone. Static analysis proposed in this paper features code reuse of repackaged malwares. We extracted DEX files from android applications and performed static analysis using class names and method names. This process doesn't not include reverse engineering, so it is possible to detect malwares efficiently.