• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.86-96
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• 2020

Based on the use cases of image recognition technology, this study looked at how artificial intelligence plays a role in image recognition technology. Through image recognition technology, satellite images can be analyzed with artificial intelligence to reveal the calculation of oil storage tanks in certain countries. And image recognition technology makes it possible for searching images or products similar to images taken or downloaded by users, as well as arranging fruit yields, or detecting plant diseases. Based on deep learning and neural network algorithms, we can recognize people's age, gender, and mood, confirming that image recognition technology is being applied in various industries. In this study, we can look at the use cases of domestic and overseas image recognition technology, as well as see which methods are being applied to the industry. In addition, through this study, the direction of future research was presented, focusing on various successful cases in which image recognition technology was implemented and applied in various industries. At the conclusion, it can be considered that the direction in which domestic image recognition technology should move forward in the future.

• Journal of the Korean Wood Science and Technology
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• v.45 no.6
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• pp.797-808
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• 2017

Automatic wood species identification systems have enabled fast and accurate identification of wood species outside of specialized laboratories with well-trained experts on wood species identification. Conventional automatic wood species identification systems consist of two major parts: a feature extractor and a classifier. Feature extractors require hand-engineering to obtain optimal features to quantify the content of an image. A Convolutional Neural Network (CNN), which is one of the Deep Learning methods, trained for wood species can extract intrinsic feature representations and classify them correctly. It usually outperforms classifiers built on top of extracted features with a hand-tuning process. We developed an automatic wood species identification system utilizing CNN models such as LeNet, MiniVGGNet, and their variants. A smartphone camera was used for obtaining macroscopic images of rough sawn surfaces from cross sections of woods. Five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch) were under classification by the CNN models. The highest and most stable CNN model was LeNet3 that is two additional layers added to the original LeNet architecture. The accuracy of species identification by LeNet3 architecture for the five Korean softwood species was 99.3%. The result showed the automatic wood species identification system is sufficiently fast and accurate as well as small to be deployed to a mobile device such as a smartphone.

• Korean journal of food and cookery science
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• v.28 no.6
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• pp.829-837
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• 2012

This study was investigated seafoods provided by school food service and students' preferences for and perceptions of seafoods. The subjects were 275 second grade(age 14-16) students of 4 middle schools in Gyeongnam. The results were as follows. The most main seafoods intake place was 'home'(65.8%). 'School food service' took meaningful ratio(20.7%) of students' seafoods intakes. In the intake amount of seafoods provided by school food service, 'all' took 22.5%(male 31.6%, female 14.1%), 'more than provided' took 1.5%(male 3.0%, female 0%). Male students ate seafoods more than female students did(p<.001). In seafoods providing frequency, '2~3 times a week' took 74.5%, '4~5 times a week' took higher ratio in males' schools, while '0~1 times a week' took higher ratio in females'(p<.05). In perceptions of seafoods, most subjects had positive perceptions as 'good for health'(3.95), 'various kinds'(3.75) except 'good peculiar smell' got smallest point(2.85). In means of learning about seafoods names, 'by looking at everyday menu' took 64.6%. In taking nutrition education, 'no nutrition education' took 69.5%. In preferences for seafoods using 5-point scale, males' preferences were higher than females'(p<.001). 48.1% of males got higher than 4 point, while 14.1% of females did. In improvement measures of seafoods, 'provide various kinds'(47.3%) took highest ratio. In preferences for seafoods by seafoods kinds, preference for 'crustacean' was highest while preferences for 'shell fish' and 'fish' were relatively low. Both male and female students highly preferred laver, shrimp, swimming crab, small octopus, fish cake and tuna canned goods. Male students' preferences were higher than female students' for most kinds of seafoods. In preferences for seafoods by cooking methods, preferences for 'grilled', 'stir fried', 'pan fried' were relatively high, 'braised', 'deep fried', 'steamed' were relatively low. Males' preferences were higher than females' for every cooking method except 'steamed'.

• Journal of Digital Contents Society
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• v.19 no.5
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• pp.1011-1019
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• 2018

Existing exploration robot is able to control its speed using technologies such as the remote control and deep learning. However its speed control method using weather information has not been proposed. To overcome the problem of conventional methods without using the weather information which is an useful ordinary life information, this paper proposes a new speed control method of exploration robot using weather information gathered from RSS service which is offered without cost by the Meteorological Agency. The exploration robot implemented in this paper is controled by the remote control through the TCP/IP communication and provides real-time real spot figure gathered from its camera sensor within the range of WiFi. Additionally, according to the weather information from URL of the Meteorological Agency, the implemented exploration robot autonomously controls it speed. The correct performance of the proposed method is verified by the experimental measurement data of its speed according to the precipitation probability and wind speed in this paper.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.20 no.1
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• pp.15-26
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• 2016

Objectives Syndrome differentiation and treatment (辨證論治) was one of the core theories in Korean medicine and syndrome differentiation (辨證) constitutes a branch of disease diagnosis in Korean medicine. Yoon Gil-Young, one of the modern outstanding scholar of basic medical science in Korean medicine, wrote on basic theories of Korean medicine such as physiology, pathology, formula science, etc. Hereby we will analyze and discuss his works to understand his recognition of historical changes in the syndrome differentiation. Methods We conducted researches into the two works of Yoon Gil-Young's, which are "The Clinical Formula Science of Eastern Medicine (東醫臨床方劑學)" and "The theory of Four-Constitution Medicine (四象體質醫學論)". From Yoon's academic standpoint which connects the basic medical science with the clinical medicine, we analyzed his opinion about the system in the Theory of 'Syndrome Differentiation'. Results According to Yoon's research work on the Theory of 'Syndrome Differentiation', the system of syndrome differentiation, which had its deep root in the theory of Yin and Yang (陰陽) & the theory of abbreviation of the five circuit phases (五運) and the six atomspheric influences (六氣) of the "Huangdi's Internal Classic (黃帝內經)". Conclusions Yoon Gil-Young's theory of differentiation of syndromes and treatment is widespread so much that he studied on the learning field of Traditional Korean Mediciine and ingenious as well. He explain on the main principles of differentiation of syndromes based on "Huang Di Nei Jing" and the system of differentiation of syndromes is composed of Traditional Korean Medical Physiology.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.551-567
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• 2021

Purpose: The purpose of this paper is to present implications by analyzing research trends on smart factories by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on smart factories. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "SMART FACTORY" and "Smart Factory" as search terms, and the titles and Korean abstracts were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, 739 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; Smart factory research slowed down from 2005 to 2014, but until 2019, research increased rapidly. According to the analysis by fields, smart factories were studied in the order of engineering, social science, and complex science. There were many 'engineering' fields in the early stages of smart factories, and research was expanded to 'social science'. In particular, since 2015, it has been studied in various disciplines such as 'complex studies'. Overall, in keyword analysis, the keywords such as 'technology', 'data', and 'analysis' are most likely to appear, and it was analyzed that there were some differences by fields and years. Conclusion: Government support and expert support for smart factories should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to smart factories. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.3
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• pp.23-29
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• 2019

Conventional crack detection methods have a problem of consuming a lot of labor, time and cost. To solve these problems, an automatic detection system is needed to detect cracks in images obtained by using vehicles or UAVs(unmanned aerial vehicles). In this paper, we have studied road crack detection with unmanned aerial photographs. Aerial images are generated through preprocessing and labeling to generate morphological information data sets of cracks. The generated data set was applied to the mask R-CNN model to obtain a new model in which various crack information was learned. Experimental results show that the cracks in the proposed aerial image were detected with an accuracy of 73.5% and some of them were predicted in a certain type of crack region.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.11-20
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• 2021

The analysis of citing and cited phrases provides an opportunity to enhance search-centric academic information services. However, most current studies focus only on citation analysis among academic associations, researchers, and articles, making it challenging to develop higher citation-based information services. This study proposes citation analysis service methods using citing and cited phrases. First, to verify the feasibility of suggested services, we have collected the most highly cited articles with specific domain terms and followed their citing relationship; after that, we found formal citation types and ratios in the original articles. And we conducted structural analysis, especially with three topics, "Deep Learning," "Green Energy," and "Aging," and then structurally illustrates the citation characteristics of related articles. Finally, we collected four most cited articles and all their citing ones for each subject from Google Scholar and analyzed the ratio of citation types and citation spread. We hope that various citation analysis studies and information services can be further developed based on our discussion for designing better information services.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.82-88
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• 2020

With the development of medical care in the 21st century and the rapid development of the 4th industry, electronic devices and household goods taking into account the physical and mental aging of the silver generation have been developed, and apps related to health and health are generally developed and operated. The apps currently used by the silver generation are a form that provides information on diseases by focusing on prevention rather than treatment, such as safety management apps for the elderly living alone and methods for preventing diseases. There are not many apps that provide information on foods that have a direct effect and nutrients in that food, and research on apps that can obtain information about individual foods is insufficient. In this paper, we propose an app that analyzes food factors and provides self-medication for health promotion of the silver generation. This app allows the silver generation to conveniently and easily obtain information such as nutrients, calories, and efficacy of food they need. In addition, this app collects/categorizes healthy food information through a textom solution-based crawling agent, and stores highly relevant words in a data resource. In addition, wide deep learning was applied to enable self-medication recommendations for food. When this technique is applied, the most appropriate healthy food is suggested to people with similar eating patterns and tastes in the same age group, and users can receive recommendations on customized healthy foods that they need before eating. This made it possible to obtain convenient healthy food information through a customized interface for the elderly through a smartphone.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.494-500
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• 2020

This paper proposes a new distance estimation technique for indoor localization in ultra wideband (UWB) systems. The proposed technique is based on recurrent neural network (RNN), one of the deep learning methods. The RNN is known to be useful to deal with time series data, and since UWB signals can be seen as a time series data, RNN is employed in this paper. Specifically, the transmitted UWB signal passes through IEEE802.15.4a indoor channel model, and from the received signal, the RNN regressor is trained to estimate the distance from the transmitter to the receiver. To verify the performance of the trained RNN regressor, new received UWB signals are used and the conventional threshold based technique is also compared. For the performance measure, root mean square error (RMSE) is assessed. According to the computer simulation results, the proposed distance estimator is always much better than the conventional technique in all signal-to-noise ratios and distances between the transmitter and the receiver.