• Proceedings of the Korea Contents Association Conference
• /
• 2008.05a
• /
• pp.819-822
• /
• 2008

As the IT and computer network technology is developed very rapidly, the quantity of digital contents is increased and disseminated more widely. The digital contents is generally expressed in 2 or 3 dimensional multimedia format and the visible human image that is taken from human body is very important because of its variety of usefulness. The KISTI(Korea Institute of Science and Technology Information) is now constructing various Korean human informations such as visible Korean, digital Korean, human bone property and human models. These informations are accessable through the internet. However, these human images are not easily understandable for general users because they are specialized in medical image field and there is no detailed explanation data. In this study, we designed the annotation database and searching interface for KISTI's visible Korean database. This annotation database involved the detailed explanation and special note of visible Korean data and it can connect the image and text data of visible Korean with each other. By studying this database and interface design, the KISTI's visible Korean database is more easily accessable and understandable to general users and it can promote the usage of visible Korean data more widely.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.11
• /
• pp.367-376
• /
• 2020

The fourth industrial revolution will greatly contribute to the entry of objects into an intelligent society through technologies such as big data and an artificial intelligence. Through the revolution, we were able to understand human behavior and awareness, and through the use of an artificial intelligence, we established ourselves as a key tool in various fields such as medicine and science. However, the fourth industrial revolution has a negative side with a positive future. In this study, an analysis was conducted using text mining techniques based on unstructured big data collected through social media. We wanted to look at keywords related to the fourth industrial revolution by year (2016, 2017 and 2018) and understand the meaning of each keyword. In addition, we understood how the keywords related to the Fourth Industrial Revolution changed with the change of the year and wanted to use R to conduct a Keyword Analysis to identify the recognition flow closely related to the Fourth Industrial Revolution through the keyword flow associated with the Fourth Industrial Revolution. Finally, people's perceptions of the fourth industrial revolution were identified by looking at the positive and negative feelings related to the fourth industrial revolution by year. The analysis showed that negative opinions were declining year after year, with more positive outlook and future.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.4
• /
• pp.37-41
• /
• 2021

This paper presents a study to implement a touch screen capable of real-time response processing in a low-end embedded system. This was done by introducing an algorithm using an interpolation method to represent real-time response characteristics when a touch input is performed. In this experiment, we applied a linear interpolation algorithm that estimates random data by deriving a first-order polynomial from 2-point data. We also applied a Lagrange interpolation algorithm that estimates random data by deriving a quadratic polynomial from 3-point data. As a result of the experiment, it was found that the Lagrange interpolation method was more complicated than the linear interpolation method, and the processing speed was slow, so the text was not smooth. When using the linear interpolation method, it was confirmed that the speed displayed on a screen is 2.4 times faster than when using the Lagrange interpolation method. For real-time response characteristics, it was confirmed that smaller size of the executable file of the algorithm is more advantageous than the superiority of the algorithm itself. In conclusion, in order to secure real-time response characteristics in a low-end embedded system, it was confirmed that a relatively simple linear interpolation algorithm performs touch operations with better real-time response characteristics than the Lagrange interpolation method.

• Journal of the Korea Convergence Society
• /
• v.11 no.12
• /
• pp.113-123
• /
• 2020

In this paper, big data analysis was conducted for past and present fashion trends and fashion cycle. We focused on daily look for ordinary people instead of the fashion professionals and fashion show. Using the social matrix tool, Textom, we performed frequency analysis, N-gram analysis, network analysis and structural equivalence analysis on the big data containing fashion trends and cycles. The results are as follows. First, this study extracted the major key words related to fashion trends for the daily look from the past(1980s, 1990s) and the present(2019 and 2020). Second, the frequence analysis and N-gram analysis showed that the fashion cycle has shorten to 30-40 years. Third, the structural equivalence analysis found the four representative clusters. The past four clusters are jean, retro codi, athleisure look, celebrity retro and the present clusters are retro, newtro, lady chic, retro futurism. Fourth, through the network analysis and N-gram analysis, it turned out that the past fashion is reproduced and evolves to the current fashion with certain reasoning.

• Knowledge Management Research
• /
• v.23 no.3
• /
• pp.129-152
• /
• 2022

As a key infrastructure for digital transformation, the spread of data, network, artificial intelligence (D.N.A.) fields and the emergence of promising industries are laying the groundwork for active digital innovation throughout the economy. In this study, by applying the text mining methodology, major topics were derived by using the abstract, publication year, and research field of the study corresponding to the SCIE, SSCI, and A&HCI indexes of the WoS database as input variables. First, main keywords were identified through TF and TF-IDF analysis based on word appearance frequency, and then topic modeling was performed using g-DMR. With the advantage of the topic model that can utilize various types of variables as meta information, it was possible to properly explore the meaning beyond simply deriving a topic. According to the analysis results, topics such as business intelligence, manufacturing production systems, service value creation, telemedicine, and digital education were identified as major research topics in digital transformation. To summarize the results of topic modeling, 1) research on business intelligence has been actively conducted in all areas after COVID-19, and 2) issues such as intelligent manufacturing solutions and metaverses have emerged in the manufacturing field. It has been confirmed that the topic of production systems is receiving attention once again. Finally, 3) Although the topic itself can be viewed separately in terms of technology and service, it was found that it is undesirable to interpret it separately because a number of studies comprehensively deal with various services applied by combining the relevant technologies.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.8
• /
• pp.241-251
• /
• 2022

Diagnosis and management of customer's skin condition is an important essential function in the cosmetics and beauty industry. As the social media environment spreads and generalizes to all fields of society, the interaction of questions and answers to various and delicate concerns and requirements regarding the diagnosis and management of skin conditions is being actively dealt with in the social media community. However, since social media information is very diverse and atypical big data, an intelligent skin condition diagnosis system that combines appropriate skin condition information analysis and artificial intelligence technology is necessary. In this paper, we developed the skin condition diagnosis system SCDIS to intelligently diagnose and manage the skin condition of customers by processing the text analysis information of social media into learning data. In SCDIS, an artificial neural network model, AnnTFIDF, that automatically diagnoses skin condition types using artificial neural network technology, a deep learning machine learning method, was built up and used. The performance of the artificial neural network model AnnTFIDF was analyzed using test sample data, and the accuracy of the skin condition type diagnosis prediction value showed a high performance of about 95%. Through the experimental and performance analysis results of this paper, SCDIS can be evaluated as an intelligent tool that can be used efficiently in the skin condition analysis and diagnosis management process in the cosmetic and beauty industry. And this study can be used as a basic research to solve the new technology trend, customized cosmetics manufacturing and consumer-oriented beauty industry technology demand.

• Smart Media Journal
• /
• v.11 no.10
• /
• pp.65-75
• /
• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.

• Journal of Intelligence and Information Systems
• /
• v.29 no.1
• /
• pp.41-63
• /
• 2023

As the online e-commerce market growing, the need for a recommender system that can provide suitable products or services to customer is emerging. Recently, many studies using the sentiment score of online review have been proposed to improve the limitations of study on recommender systems that utilize only quantitative information. However, this methodology has limitation in extracting specific preference information related to customer within online reviews, making it difficult to improve recommendation performance. To address the limitation of previous studies, this study proposes a novel recommendation methodology that applies deep learning technique and uses various linguistic factors within online reviews to elaborately learn customer preferences. First, the interaction was learned nonlinearly using deep learning technique for the purpose to extract complex interactions between customer and product. And to effectively utilize online review, cognitive contents, affective contents, and linguistic style matching that have an important influence on customer's purchasing decisions among linguistic factors were used. To verify the proposed methodology, an experiment was conducted using online review data in Amazon.com, and the experimental results confirmed the superiority of the proposed model. This study contributed to the theoretical and methodological aspects of recommender system study by proposing a methodology that effectively utilizes characteristics of customer's preferences in online reviews.

• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.249-265
• /
• 2023

Recently, the satellite industry has been paying attention to the private-led 'New Space' paradigm, which is a departure from the traditional government-led industry. The space industry, which is considered to be the next food industry, is still receiving relatively little attention in Korea compared to the global market. Therefore, the purpose of this study is to explore future signals that can help determine the market entry strategies of private companies in the domestic satellite industry. To this end, this study utilizes the theoretical background of future signal theory and the Keyword Portfolio Map method to analyze keyword potential in patent document data based on keyword growth rate and keyword occurrence frequency. In addition, news data was collected to categorize future signals into first symptom and early information, respectively. This is utilized as an interpretive indicator of how the keywords reveal their actual potential outside of patent documents. This study describes the process of data collection and analysis to explore future signals and traces the evolution of each keyword in the collected documents from a weak signal to a strong signal by specifically visualizing how it can be used through the visualization of keyword maps. The process of this research can contribute to the methodological contribution and expansion of the scope of existing research on future signals, and the results can contribute to the establishment of new industry planning and research directions in the satellite industry.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.8
• /
• pp.371-380
• /
• 2023

Relation extraction is to extract relationships between named entities from text. Traditionally, relation extraction methods only extract relations between predetermined subject and object entities. However, in end-to-end relation extraction, all possible relations must be extracted by considering the positions of the subject and object for each pair of entities, and so this method uses time and resources inefficiently. To alleviate this problem, this paper proposes a method that sets directions based on the positions of the subject and object, and extracts relations according to the directions. The proposed method utilizes existing relation extraction data to generate direction labels indicating the direction in which the subject points to the object in the sentence, adds entity position tokens and entity type to sentences to predict the directions using a pre-trained language model (KLUE-RoBERTa-base, RoBERTa-base), and generates representations of subject and object entities through probabilistic crossover operation. Then, we make use of these representations to extract relations. Experimental results show that the proposed model performs about 3 ~ 4%p better than a method for predicting integrated labels. In addition, when learning Korean and English data using the proposed model, the performance was 1.7%p higher in English than in Korean due to the number of data and language disorder and the values of the parameters that produce the best performance were different. By excluding the number of directional cases, the proposed model can reduce the waste of resources in end-to-end relation extraction.