• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.555-559
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• 2006

This paper will intend to contribute to extracting and storing various form of information on user interests by using structural rules user makes and XML-based word document converting techniques. The system named PPE consists of three essential element. One is converting element which converts word documents like HWP, DOC into XML documents, another is extracting element to prepare structural rules and extract concerned information from XML document by structural rules, and the other is storing element to make final XML document or store it into database system. For word document converting, we developed OCX based word converting daemon. Helping user to extracting information, we developed script language having native function/variable processing engine extended from XSLT. This system can be used in the area of constructing word document contents DB or providing various information service based on RAW word documents. We really applied it to project management system and project result management system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.2
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• pp.689-695
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• 2010

The effect of the code conversion process of Korean script (Hangul), also known as words for numbers, was investigated using event-related potentials (ERPs) during mental arithmetic operations. Study subjects were asked to determine whether the arithmetic results of a given target stimuli were correctly matched. Visual inspection and statistics of mean ERPs showed stimulus type-dependent processing rather than task-dependent processing. Results of addition and multiplication tasks revealed that the overall temporal profiles of the Arabic numerals were similar to the Hangul words for numbers. The only exception to this observation was a delayed positive-slope peak occurring around 300 ms, which was likely related to the encoding process of Hangul words for numbers to Arabic-digits, defined as a 'transcoding-related potential.' Source analysis confirmed that the topography of different waveforms for the two conditions was attributed to a single dipole located in the left temporo-parietal area; this area is known to be involved in Hangul words for number processing. These results suggest that the initial processing for encoding words for numbers was followed by arithmetic operations without direct access of internal number representation. Korea Academia-Industrial cooperation Society. The Korea Academia-Industrial cooperation Society. The Korea Academia-Industrial cooperation Society. The Korea Academia-Industrial cooperation Society.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1105-1112
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• 2002

Nowadays the information exchange based on XML such as B2B electronic commerce is spreading. Therefore a systematic and stable management mechanism for storing the exchanged information is needed. For this goal there are many research activities for concerning the connection between XML application and relational databases. But because XML data has hierarchical structure and relational databases can store only flat-structured data, we need to make a conversion rule which changes the hierarchical architecture to a 2-dimensional format. Accordingly the modeling methodology for storing such structured information in relational databases is needed. In order to build good quality application systems, modeling is an important first step. In 1997, the OMG adopted the UML as its standard modeling language. Since industry has warmly embraced UML, its popularity should become more important in the future. So a design methodology based on UML is needed to develop efficient XML applications. In this paper, we propose a unified design methodology for XML applications based on relational database using UML. To reach these goals, first we introduce a XML modeling methodology to design W3C XML schema using UML and second we propose data modeling methodology for relational database schema to store XML data efficiently in relational databases.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1271-1282
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• 2005

Recently, PC-based control is incredibly developed in the industrial control field, but it is difficult for PLC programming in PC. Therefore, I need to develop the softeware PLC, which support the international PLC programming standard(IECl131-3) and can be applied to diverse control system by using C language. In this paper, I have developed the ISPLC(Intelligent Agent System based Software Programmable Logic Controller). In ISPLC system, LD programmed by a user which is used over $90\%$ among the 5 PLC languages, is converted to IL, which is one of intermediate codes, and IL is converted to the standard C rode which can be used in a commercial editor such as Visual C++. In ISPLC, the detection of logical error in high level programming(C) is more eaier than PLC programming itself The study of code conversion of LD->IL->C is firstly tried in the world as well as KOREA. I developed an execution engine with a good practical application. To show the effectiveness of the developed system, 1 applied it to a practical case, a real time traffic control(RT-TC) system. ISPLC is minimized the error debugging and programming time owing to be supported by windows application program.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.95-104
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• 2021

This study was conducted for the purpose of convergence reconstruction of the transition education model for students with disabilities in Korea. Ultimately, this study was also conducted with the aim of enhancing the perspective of the development of a lifelong education support system for the disabled in connection with schools and communities. The research method consisted of a procedure with a meeting of experts based on the procedure of analyzing the previous research literature that tried to materialize the transition education model for students with disabilities from the viewpoint of connection between school age and adulthood. The contents of this study were reflected in the dimension of ensuring consistent connectivity validity based on the viewpoint of school-centered, community-centered, education, and welfare between special education and lifelong education for the disabled in order to reconstruct the conversion transition education model constructed in the current special education field. Accordingly, the transition education model for students with disabilities built in the field of special education centered on school age minimizes the tendency of a fragmented approach between school age and adulthood, and presents a standard basis and structure that can be linked to the entire adulthood. The transition education model was reconstructed convergence in terms of content.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.187-192
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• 2023

In this study, trends in ICT education were investigated by analyzing the frequency of appearance of keywords related to machine learning and using conversion of iteration correction(CONCOR) techniques. A total of 304 papers from 2018 to the present published in registered sites were searched on Google Scalar using "ICT education" as the keyword, and 60 papers pertaining to ICT education were selected based on a systematic literature review. Subsequently, keywords were extracted based on the title and summary of the paper. For word frequency and indicator data, 49 keywords with high appearance frequency were extracted by analyzing frequency, via the term frequency-inverse document frequency technique in natural language processing, and words with simultaneous appearance frequency. The relationship degree was verified by analyzing the connection structure and centrality of the connection degree between words, and a cluster composed of words with similarity was derived via CONCOR analysis. First, "education," "research," "result," "utilization," and "analysis" were analyzed as main keywords. Second, by analyzing an N-GRAM network graph with "education" as the keyword, "curriculum" and "utilization" were shown to exhibit the highest correlation level. Third, by conducting a cluster analysis with "education" as the keyword, five groups were formed: "curriculum," "programming," "student," "improvement," and "information." These results indicate that practical research necessary for ICT education can be conducted by analyzing ICT education trends and identifying trends.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.