• Smart Media Journal
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• v.3 no.2
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• pp.20-28
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• 2014

The issues of generation, delivery, and processing of information which have been treated importantly in information design field have evolved along with the evolution of the humankind. In the modern society, the vast amount of, complex, and artificial forms of information such as big-data is accounted for the majority and claims of interest focusing on how to effectively design those kinds of information are being increased. This study explored the visualization methods applied with the natural objects as motives as one of the ways for users to easily get their perception and cognition to the information. Nature has long influenced on the human figural activities. The natural objects take the optimum visual shapes and provide the diverse inspiration and emotion to the designers in the various design fields such as product design, architecture design, and so on. Through the literature studies, we suggested the compositional principles of natural objects and the principles for observing and analysing natural objects as a principle to use the natural objects for information design domain. We, also, suggested the information design approach model which is inspired the natural objects by linking those two kinds of principles to the information design's visual realization factors and explored the possibilities of utilizing of the approach model by the case studies.

• Journal of Convergence for Information Technology
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• v.10 no.11
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• pp.23-29
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• 2020

As IoT devices increase exponentially, minimizing MIMO interference and increasing transmission capacity for sending and receiving IoT information through multiple antennas remain the biggest issues. In this paper, secret key-level distribution mechanism using deep learning is proposed to minimize MIMO-based IoT communication noise. The proposed mechanism minimizes resource loss during transmission and reception process by dispersing IoT information sent and received through multiple antennas in batches using deep learning. In addition, the proposed mechanism applied a multidimensional key distribution processing process to maximize capacity through multiple antenna multiple stream transmission at base stations without direct interference between the APs. In addition, the proposed mechanism synchronizes IoT information by deep learning the frequency of use of secret keys according to the number of IoT information by applying the method of distributing secret keys in dimension according to the number of frequency channels of IoT information in order to make the most of the multiple antenna technology.

• The Research Journal of the Costume Culture
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• v.19 no.1
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• pp.83-95
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• 2011

Today, a variety performance premiered in Korea, works of art as an advanced production planning and production system is becoming. Accordingly, the field of stage costume also increased collaboration with foreign producers and production systems and the advancement of the stage costume are required are becoming. The opening performance of the 30th anniversary of the Seoul Theater Festival was selected as Towol Theater Theater in 2009, April 16 to 26 of Picasso's women's costume is the study of production systems. This work directing and stage design by inviting domestic producers from foreign fields, and co-authored the work in the field of stage costumes in collaboration with foreign producers that are worth study and research work. In this study, this work has a practical study of costumes throughout the production system, an advanced stage costumes to contribute to the development of the field. The research methodology book data, collected papers, Internet resources through research and theoretical studies play 'Picasso's women's stage production of the award total to an empirical study was undertaken. The results of this study are as follows. First, the costume director for making a scholarly grasp of the direction of the investigation is ongoing throughout the process of creating the costumes. Second, foreign producers and co-author of the stage when the award, if other than purely domestic producers and create costumes to build production systems. Third, foreign producers and co-costume design and costume making coherent explanation for the processing of the list(Costume Breakdown List) are developed. Fourth, the actual performance over the director's intention to visualize the presentation was good enough, and the idea of the costume crew was taken to the director's idea of directing a play that reflected the will has a big meaning.

• The Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.105-116
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• 2019

Most of the public and private buildings in Korea are installing CCTV for crime prevention and follow-up action, insider security, facility safety, and fire prevention, and the number of installations is increasing each year. In the questionnaire conducted on the increasing CCTV, many reactions were positive in terms of the prevention of crime that could occur due to the installation, rather than negative views such as privacy violation caused by CCTV shooting. However, CCTV poses a lot of privacy risks, and when the image data is collected using the cloud, the personal information of the subject can be leaked. InseCam relayed the CCTV surveillance video of each country in real time, including the front camera of the notebook computer, which caused a big issue. In this paper, we introduce a system to prevent leakage of private information and enhance the security of the cloud system by processing the privacy technique on image information about a subject photographed through CCTV.

• Food Science and Industry
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• v.54 no.4
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• pp.293-300
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• 2021

Food safety accidents have been increasing by 2% over 5,000 cases every year since 2009. Most people know that the best method to prevent food safety accidents is a quick inspection, but there is a lack of inspection technology that can be used at the non-analytic level to food production and distribution sites. Among the recent on-site diagnostic technologies, the methods for testing gene-based food poisoning bacteria were introduced with the STA technology, which can range from sample to detection. If food safety information can be generated without forgery by directly inspecting food hazard factors by remote, unmanned, not human, pollution sources can be managed by predicting risks more accurately from current big-data and artificial intelligence technology. Since this information processing can be used on smartphones using the current cloud technology, it is judged that it can be used for food safety to small food businesses or catering services.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.819-834
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• 2022

Artificial Intelligence (AI) techniques have been effectively used for image classification, object detection, and image segmentation. Along with the recent advancement of computing power, deep learning models can build deeper and thicker networks and achieve better performance by creating more appropriate feature maps based on effective activation functions and optimizer algorithms. This review paper examined technical and academic trends of Convolutional Neural Network (CNN) and Transformer models that are emerging techniques in remote sensing and suggested their utilization strategies and development directions. A timely supply of satellite images and real-time processing for deep learning to cope with disaster monitoring will be required for future work. In addition, a big data platform dedicated to satellite images should be developed and integrated with drone and Closed-circuit Television (CCTV) images.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.6
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• pp.189-196
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• 2023

Today, in the era of the 4th industrial revolution based on the paradigm of hyper-connectivity, super-intelligence, and superconvergence, the remote work environment is becoming central based on technologies such as mobile, cloud, and big data. This remote work environment has been accelerated by the demand for non-face-to-face due to COVID-19. Since the remote work environment can perform various tasks by accessing services and resources anytime and anywhere, it has increased work efficiency, but has caused a problem of incapacitating the traditional boundary-based network security model by making the internal and external boundaries ambiguous. In this paper, we propse a method to improve the limitations of the traditional boundary-oriented security strategy by building a security model centered on core components and their relationships based on the zero trust idea that all actions that occur in the network beyond the concept of the boundary are not trusted.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.71-96
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• 2023

This study aimed to investigate key research areas in the field of Library and Information Science (LIS) by analyzing trends and identifying emerging topics. To facilitate the research, a collection of 40,897 author keywords from 11,252 papers published in the past 30 years (1993-2022) in five journals was gathered. In addition, keyword analysis, as well as Principal Component Analysis (PCA) and correlation analysis were conducted, utilizing variables such as the number of articles, number of authors, ratio of co-authored papers, and cited counts. The findings of the study suggest that two topics are likely to develop as promising research areas in LIS in the future: machine learning/algorithm and research impact. Furthermore, it is anticipated that future research will focus on topics such as social media and big data, natural language processing, research trends, and research assessment, as they are expected to emerge as prominent areas of study.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.155-164
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• 2023

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this review.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.