• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.90-99
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• 2009

This paper presents the implementation of RFID EPC global network educational system based on using 900MHz air interface between the reader and the active tag. The software of reader and the active tag is developed on embedded environment, and the software of PC controlling the reader is based on window OS operated as the server. The ATmega128 VLSI chip is used for the processor of the reader and the active tag. As the development environment, AVR compiler is used for the reader and the active tag of which the programming language is C. The visual C++language of the visual studio on the PC activated as the server is used for development language. Main functions of this system are to control tag containing EPC global Data by PC through the reader, to obtain information of tag through the internet and to read/write data on tag memory. Finally the data written from the active tag's memory is sent to the PC via the reader as "read" operation and compare the received data with one already sent to the tag. Software implementation of 900MHz EPC global RFID educational system is done on the basis of these functions.

• Nutrition Research and Practice
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• v.17 no.6
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• pp.1255-1266
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• 2023

BACKGROUND/OBJECTIVES: This study aimed to predict the association between nutritional intake and diabetes mellitus (DM) by developing an artificial neural network (ANN) model for older adults. SUBJECTS/METHODS: Participants aged over 65 years from the 7th (2016-2018) Korea National Health and Nutrition Examination Survey were included. The diagnostic criteria of DM were set as output variables, while various nutritional intakes were set as input variables. An ANN model comprising one input layer with 16 nodes, one hidden layer with 12 nodes, and one output layer with one node was implemented in the MATLAB^® programming language. A sensitivity analysis was conducted to determine the relative importance of the input variables in predicting the output. RESULTS: Our DM-predicting neural network model exhibited relatively high accuracy (81.3%) with 11 nutrient inputs, namely, thiamin, carbohydrates, potassium, energy, cholesterol, sugar, vitamin A, riboflavin, protein, vitamin C, and fat. CONCLUSIONS: In this study, the neural network sensitivity analysis method based on nutrient intake demonstrated a relatively accurate classification and prediction of DM in the older population.

• The Journal of the Korea Contents Association
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• v.8 no.11
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• pp.412-425
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• 2008

One of the important elements for improving academic achievement of learners in education through e-learning is to support learners to study by finding questions they want with providing various evaluation questions. However, most of question retrieval systems usually depend on keyword search based on only a syntactical analysis and/or a hierarchical browsing system classified by the topics of subjects. In such a system it is not easy to find integrative questions associated with each other. In order to improve this problem, in this paper we proposed a question management and retrieval system which allows users to easily manage questions and also to effectively find questions for study on the Web. Then, we implemented a system that gives to access questions for the domain of C language programming. The system makes it possible to easily search questions related to not only a single theme but also questions integrated by interrelationship between topics and questions. This is done by supporting to be able to retrieve questions according to conceptual interrelationships between questions from user query. Consequently, it is expected that the proposed system will provide learners to understand the basic theories and the concepts of the subjects as well as to improve the ability of comprehensive knowledge utilization and problem-solving.

• Economic and Environmental Geology
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• v.54 no.2
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• pp.233-245
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• 2021

Many studies on the microstructures in rocks have been conducted using experimental methods with various equipment as well as natural rock studies to see the development of microstructures and understand their mechanisms. Grain boundary migration of mineral aggregates in rocks could cause grain growth or grain size changes during metamorphism or deformation as one of the main recrystallization mechanisms. This study suggests improved ways regarding the analog material experiments with reformed equipment to see sequential observations of these grain boundary migration. It can be more efficient than the existing techniques and carry out an appropriate microstructure analysis. This reformed equipment was implemented to enable optical manipulation by mounting polarizing plates capable of rotating operation on a stereoscopic microscope and a deformation rig capable of experimenting with analog materials. The equipment can automatically control the temperature and strain rate of the deformation rig by microcontrollers and programming and can take digital photomicrographs with constant time intervals during the experiment to observe any microstructure changes. The composite images synthesized using images by rotated polarizing plates enable us to see more accurate grain boundaries. As a rock analog material, norcamphor(C7H10O) was used, which has similar birefringence to quartz. Static grain growth and simple shear deformation experiments were performed using the norcamphor to verify the effectiveness of the equipment. The static grain growth experiments showed the characteristics of typical grain growth behavior. The number of grains decreases and the average grain size increases over time. These case experiments also showed a clear difference between the growth curves with three temperature conditions. The result of the simple shear deformation experiment under the medium temperature-low strain rate showed no significant change in the average grain size but presented the increased elongation of grain shapes in the direction of about 53° regarding the direction perpendicular to the shearing direction as the shear strain increases over time. These microstructures are interpreted as both the plastic deformation and the internal recovery process in grains are balanced by the deformation under the given experimental conditions. These experiments using the reformed equipment represent the ability to sequentially observe changing the microstructure during experiments as desired in the tests with the analog material during the entire process.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.