• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.3
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• pp.664-673
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• 2008

Recently, the ubiquitous is handled by maximum topic. New knowledge information and ubiquitous computing evolution have promoted new paradigm transfer and grand change. Also, need technology as powerful engineering approached fairly system and educational guidance side examination necessarily to overcome u-Learning base situation and studying obstacle situations. This treatise embodied handiness examination about attention shortage and excess obstacle (Attention Deficit Hyperactivity Disorder, low ADHD) who must solve so as to be square and level being increase trend in primary school using USB (Universal Serial Bus) terminal system that allow fetters to OSGi (Open Service Gateway Initiative). That OSGi base USB terminal system is easy preservation of information, safety of network, cost-cutting and maintenance by various ubiquitous system that server that load many USB terminals and OSGi uses an USB bus of high speed and construct network, there is advantage of concentration elevation and so on of week and ADHD handled in this treatise because early diagnosis and treatment are serious. The confirmed system application that can supplement paper and pens examination's shortcoming and could solve examination's problem which use computer, and help in student guidance through ADHD simpleexamination who utilize OSGi base USB terminal system. Is available by game system that system for human nature examination or intelligence test and general exam explaining and level studying, order style question investigation program, studying system for disabled person, majority that enforce in public in school this study finding does together.

• The Journal of Korean Association of Computer Education
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• v.5 no.1
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• pp.1-16
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• 2002

The development of information and communications technologies(ICT) is changing school education, which is a center of teaching/lession process. Information and communications technologies can not guarantee quality education appropriate for knowledge & information society. Interactions between ICT and educational environment, change in the role of teachers, and shift in teaching strategies for educational contents and learning method would be required. This paper has studied the relationship between school education and ICT, change in the role of teachers, and a direction in teaching strategies to take advantage of ICT in school education. For this purpose, it has endeavored to offer an ideal ICT environment by researching both some cases in the foreign countries and the seventh educational process in Korea. In conclusion, this study recommends the followings; First, interactive environment between school and ICT is necessary to make education appropriate to knowledge-information society; Secondly, in the structutive teaching/learing process based upon ICT classroom, teachers should not be the old role player, such as knowledge transfer and learning manager any longer; instead, they should stimulate more social and conversational thinking, and integrate ICT into teaching process; Thirdly, teaching strategies need to change for the purpose of promoting evaluative thinking productive thinking creative thinking.

• Journal of Science Education
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• v.45 no.1
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• pp.1-10
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• 2021

In this study, we analyzed the relationship between the physical science domain of TIMSS 2019 and the Korean science curriculum. Twelve subjects are presented in the physical science domain of the TIMSS 2019 4th grade evaluation framework. Research group consisting of elementary and middle school teachers and science education experts, a total of 12, participated to analyze in which grade these subjects were included in the Korea 2009 revised and 2015 revised science curriculum. As a results of analyzing whether the achievement standards of the TIMSS 2019 evaluation framework and Korean science curriculum are consistent, the subjects pertinent to chemistry like 'chemical changes in everyday life,' 'heat transfer,' and 'electricity and simple electrical circuits' appeared not covered at all until the 4th grade curriculum in Korea. Given that the TIMSS 2019 evaluation framework is an international achievement standard, we are proposing to improve the Korean curriculum as follows: first, for the development of the next science curriculum, there is a need for science curriculum organized from the 1st grade of elementary school to connect the content and scope of chemistry in elementary, middle, and high schools as a whole including the Nuri curriculum. Second, as an alternative to the problem of suitability of learning volume and level of learning, it is possible to think of a method to readjust the grade of dealing with related concepts by lowering the difficulty or simplifying the concept. Third, it is necessary to discuss about introducing essential concepts and standard terms into Korea science curriculum according to international trends.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1719-1729
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• 2021

Leaf area index (LAI) provides valuable information necessary for sustainable and effective management of forests. Although global high resolution LAI data are provided by European Space Agency using Sentinel-2 satellite images, they have not considered forest characteristics in model development and have not been evaluated for various forest ecosystems in South Korea. In this study, we proposed a LAI estimation model combining machine learning and the PROSAIL radiative transfer model using Sentinel-2 satellite data over a local forest area in South Korea. LAI-2200C was used to measure in situ LAI data. The proposed LAI estimation model was compared to the existing Sentinel-2 LAI product. The results showed that the proposed model outperformed the existing Sentinel-2 LAI product, yielding a difference of bias ~ 0.97 and a difference of root-mean-square-error ~ 0.81 on average, respectively, which improved the underestimation of the existing product. The proposed LAI estimation model provided promising results, implying its use for effective LAI estimation over forests in South Korea.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.2
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• pp.435-452
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• 2023

This study experimented with automatic classification of subject headings using BERT-based transfer learning model, and analyzed its performance. This study analyzed the classification performance according to the main class of KDC classification and the category type of subject headings. Six datasets were constructed from Korean national bibliographies based on the frequency of the assignments of subject headings, and titles were used as classification features. As a result, classification performance showed values of 0.6059 and 0.5626 on the micro F1 and macro F1 score, respectively, in the dataset (1,539,076 records) containing 3,506 subject headings. In addition, classification performance by the main class of KDC classification showed good performance in the class General works, Natural science, Technology and Language, and low performance in Religion and Arts. As for the performance by the category type of the subject headings, the categories of plant, legal name and product name showed high performance, whereas national treasure/treasure category showed low performance. In a large dataset, the ratio of subject headings that cannot be assigned increases, resulting in a decrease in final performance, and improvement is needed to increase classification performance for low-frequency subject headings.

• Korean Chemical Engineering Research
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• v.59 no.2
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• pp.209-218
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• 2021

First principle-based modeling studies have been performed to improve the heat exchange efficiency of ORV and optimize operation, but the heat transfer coefficient of ORV is an irregular system according to time and location, and it undergoes a complex modeling process. In this study, FNN, LSTM, and AutoML-based modeling were performed to confirm the effectiveness of data-based modeling for complex systems. The prediction accuracy indicated high performance in the order of LSTM > AutoML > FNN in MSE. The performance of AutoML, an automatic design method for machine learning models, was superior to developed FNN, and the total time required for model development was 1/15 compared to LSTM, showing the possibility of using AutoML. The prediction of NG and seawater discharged temperatures using LSTM and AutoML showed an error of less than 0.5K. Using the predictive model, real-time optimization of the amount of LNG vaporized that can be processed using ORV in winter is performed, confirming that up to 23.5% of LNG can be additionally processed, and an ORV optimal operation guideline based on the developed dynamic prediction model was presented.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.10
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• pp.299-308
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• 2023

As the spectrum shortage problem has accelerated by the emergence of various services, New Radio-Unlicensed (NR-U) has appeared, allowing users who communicated in licensed bands to communicate in unlicensed bands. However, NR-U network users reduce the performance of Wi-Fi network users who communicate in the same unlicensed band. In this paper, we aim to simultaneously maximize the fairness and throughput of the unlicensed band, where the NR-U network users and the WiFi network users coexist. First, we propose an optimal power allocation scheme based on Monte Carlo Policy Gradient of reinforcement learning to maximize the sum of rates of NR-U networks utilizing rate-splitting multiple access in unlicensed bands. Then, we propose a channel occupancy time division algorithm based on sequential Raiffa bargaining solution of game theory that can simultaneously maximize system throughput and fairness for the coexistence of NR-U and WiFi networks in the same unlicensed band. Simulation results show that the rate splitting multiple access shows better performance than the conventional multiple access technology by comparing the sum-rate when the result value is finally converged under the same transmission power. In addition, we compare the data transfer amount and fairness of NR-U network users, WiFi network users, and total system, and prove that the channel occupancy time division algorithm based on sequential Raiffa bargaining solution of this paper satisfies throughput and fairness at the same time than other algorithms.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.199-205
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• 2024

Purpose: This study aims to improve the recognition rate of Auto People Counting (APC) in accurately identifying and providing information on remaining evacuees in disaster-vulnerable facilities such as nursing homes to firefighting and other response agencies in the event of a disaster. Methods: In this study, a baseline model was established using CNN (Convolutional Neural Network) models to improve the algorithm for recognizing images of incoming and outgoing individuals through cameras installed in actual disaster-vulnerable facilities operating APC systems. Various algorithms were analyzed, and the top seven candidates were selected. The research was conducted by utilizing transfer learning models to select the optimal algorithm with the best performance. Results: Experiment results confirmed the precision and recall of Densenet201 and Resnet152v2 models, which exhibited the best performance in terms of time and accuracy. It was observed that both models demonstrated 100% accuracy for all labels, with Densenet201 model showing superior performance. Conclusion: The optimal algorithm applicable to APC among various artificial intelligence algorithms was selected. Further research on algorithm analysis and learning is required to accurately identify the incoming and outgoing individuals in disaster-vulnerable facilities in various disaster situations such as emergencies in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.568-578
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• 2021

Patents that are research results from industry-university cooperation (IUC) are a source of innovation, and play an important role in economic growth, such as technology transfer and commercialization. For this reason, there are many efforts to revitalize IUC, but in general, company patents are achievements that can be commercialized, rather than research achievements, so not all patents are used for business, even after their creation as the outcome of IUC. Therefore, this research supports the design of measures in which IUC can ultimately be linked to successful utilization of patents by identifying the purposes of IUC, even after it has been successfully promoted, and patents have been filed as a result. To this end, first, the patents registered for industry-academia cooperation in the United States are collected, and second, a predictive model is designed, with unexpired and expired patents predicted using machine learning techniques. The final identified patents are intended to derive available factors in terms of marketability and technicality. This study is expected to help predict the utilization of unexpired and expired patents, and is expected to contribute to setting goals for research results from technical cooperation between corporate and university officials planning early IUC.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.17-25
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• 2023

In this paper, we propose a model that can perform human pose estimation through a MobileViT-based model with fewer parameters and faster estimation. The based model demonstrates lightweight performance through a structure that combines features of convolutional neural networks with features of Vision Transformer. Transformer, which is a major mechanism in this study, has become more influential as its based models perform better than convolutional neural network-based models in the field of computer vision. Similarly, in the field of human pose estimation, Vision Transformer-based ViTPose maintains the best performance in all human pose estimation benchmarks such as COCO, OCHuman, and MPII. However, because Vision Transformer has a heavy model structure with a large number of parameters and requires a relatively large amount of computation, it costs users a lot to train the model. Accordingly, the based model overcame the insufficient Inductive Bias calculation problem, which requires a large amount of computation by Vision Transformer, with Local Representation through a convolutional neural network structure. Finally, the proposed model obtained a mean average precision of 0.694 on the MS COCO benchmark with 3.28 GFLOPs and 9.72 million parameters, which are 1/5 and 1/9 the number compared to ViTPose, respectively.