• Journal of the Geological Society of Korea
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• v.54 no.6
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• pp.615-630
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• 2018

Basin analysis is a research field to understand the formation and evolution of sedimentary basins. This task requires various geoscientific datasets as well as numerical and graphical modelling techniques to synthesize results dimensionally in time and space. For basin analysis and modelling in a comprehensive workflow, BasinVis 1.0 was released as a MATLAB-based program in 2016, and recently the software has been extended to BasinVis 2.0, with new functions and revised user-interface. As a case study, this work analyses the southern Vienna Basin and visualizes the sedimentation setting and subsidence evolution to introduce the basin modelling functions of BasinVis 2.0. This is a preliminary study for a basin-scale modelling of the Vienna Basin, together with our previous studies using BasinVis 1.0. In the study area, during the late Early Miocene, sedimentation and subsidence are significant along strike-slip and en-echelon listric normal faults. From the Middle Miocene onwards, however, subsidence decreases abruptly over the area and this situation continues until the Late Miocene. This is related to the development of the pull-apart system and corresponds to the episodic tectonic subsidence in strike-slip basins. The subsidence of the Middle Miocene is confined mainly to areas along the strike-slip faults, while, from the late Middle Miocene, the depocenter shifts to a depression along the N-S trending listric normal faults. This corresponds to the regional paleostress regime transitioning from NE-SW trending transtension to E-W trending extension. This study applies various functions and techniques to this case study, and the modelled results demonstrate that BasinVis 2.0 is effective and applicable to the basin modelling.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.57-65
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• 2010

This paper describes the method of efficient container number recognition in colored container image with number plate at ATC(Automatic Transfer Crane) in container terminal yard. At the Sinseondae terminal gate in Busan, the container number recognition system is installed by "intelligent port-logistics system technology development", that is government research and development project. It is the method that it sets up the tunnel structure inside camera on the gate and it recognizes the container number in order to recognize the export container cargo automatically. However, as the automation equipment is introduced to the container terminal and the unmanned of a task is gradually accomplished, the container number recognition system for the confirmation of the object of work is required at ATC in container terminal yard. Therefore, the container number recognition system fitted for it is necessary for ATC in container terminal yard in which there are many intrusive of the character recognition through image including a sunlight, rain, snow, shadow, and etc. unlike the gate. In this paper, hardware components of the camera, illumination, and sensor lamp were altered and software elements of an algorithm were changed. that is, the difference of the brightness of the surrounding environment, and etc. were regulated for recognize a container number. Through this, a shadow problem, and etc. that it is thickly below hung with the sunlight or the cargo equipment were solved and the recognition time was shortened and the recognition rate was raised.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.1
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• pp.1-12
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• 2019

In this paper, it was found that most of the machinery facilities problems generated by clients could be prevented in advance by systematically managing the mechanical equipment production process of small and medium enterprises (SMEs) that produce machinery facilities. Major point of this process is to establish an operating system that corresponds to reality of facility manufacturers as it represents 63% of machinery facilities problems that occur in customers and is a task that needs to be solved most intensively. Technical issues account for 23% of machinery facilities problems occurring at the client's companies and should be approached from a long-term perspective as they are directly related to the technical capabilities of the manufacturers. Organizational problems account for 14% of machinery facilities problems occurring in customer companies, and can change depending on the relationship of members and the nature of the human being, such as morality and motivation. In addition, we propose the establishment of an Internet-based production process management platform for smooth and efficient transfer of information between customers and machinery facilities manufacturers.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.111-118
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• 2019

For the security of a vast amount of information, it has been started to diagnose the site as a way of operating and managing the information owned by a company holding assets, to establish indexes to check the actual status and all kinds of standards to obtain security, and also to classify the information assets based on that. This has been extended to many different areas including policies to operate and manage information assets, services, the management of owned devices as physical assets, and also the management of logical assets for application software and platforms. Some of these information assets are already being operated in reality as new technology in new areas, for example, Internet of Things. Of course, a variety of electronic devices like Smart Home are being used in ordinary families, and unlike in the past, these devices generate a series of information life cycles such as accumulating and processing information. Moreover, as even distribution is now being realized, we are facing a task to secure the stability of information assets and also information that assets are holding. The purpose of this study is to suggest and apply standard security policy by moduling methods for information assets owned by companies and even families and obtain the enhancement of confidentiality as well as integrity.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.213-222
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• 2019

Determining the meaning of a keyword in a speech dialogue system is an important technology for the future implementation of an intelligent speech dialogue interface. After extracting keywords to grasp intention from user's utterance, the intention of utterance is determined by using the semantic mark of keyword. One keyword can have several semantic marks, and we regard the task of attaching the correct semantic mark to the user's intentions on these keyword as a problem of word sense disambiguation. In this study, about 23% of all keywords in the corpus is manually tagged to build a semantic mark dictionary, a synonym dictionary, and a context vector dictionary, and then the remaining 77% of all keywords is automatically tagged. The semantic mark of a keyword is determined by calculating the context vector similarity from the context vector dictionary. For an unregistered keyword, the semantic mark of the most similar keyword is attached using a synonym dictionary. We compare the performance of the system with manually constructed training set and semi-automatically expanded training set by selecting 3 high-frequency keywords and 3 low-frequency keywords in the corpus. In experiments, we obtained accuracy of 54.4% with manually constructed training set and 50.0% with semi-automatically expanded training set.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.1-8
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• 2021

In the political field of news articles, there are polarized and biased characteristics such as conservative and liberal, which is called political bias. We constructed keyword-based dataset to classify bias of news articles. Most embedding researches represent a sentence with sequence of morphemes. In our work, we expect that the number of unknown tokens will be reduced if the sentences are constituted by subwords that are segmented by the language model. We propose a document embedding model with subword tokenization and apply this model to SVM and feedforward neural network structure to classify the political bias. As a result of comparing the performance of the document embedding model with morphological analysis, the document embedding model with subwords showed the highest accuracy at 78.22%. It was confirmed that the number of unknown tokens was reduced by subword tokenization. Using the best performance embedding model in our bias classification task, we extract the keywords based on politicians. The bias of keywords was verified by the average similarity with the vector of politicians from each political tendency.

• Journal of Korean Library and Information Science Society
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• v.52 no.2
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• pp.145-166
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• 2021

The study was aimed at verifying the effectiveness of Book-Coding reading education as a reader activity of older elementary school children at a time when high-dimensional thinking abilities were formed. To this end, 30 fifth-grade children of N Elementary School in N-si, Gyeonggi-do, comprised of 15 students from a reading education program using Book-Coding, and 15 students from a reading comprehension program, and applied the reading education program over a total of 12 sessions. The main results of the study are summarized as follows. First, when the effects of the convergence reading education program using Book-Coding on the logical thinking ability of the students in the upper grades in the elementary school were analyzed, all the six sub-factors of logical thinking ability, that is, conservation logic, proportional logic, variable controlling logic, probabilistic logic, correlational inference logic, and combinational logic, were proved to have statistically more meaningful difference than the group writing a book report. Second, the analysis result of the influence of the convergence reading education program using Book-Coding on the creativity of the students in the upper grades of the elementary school showed that all the 13 elements of curiosity, persistence, effectiveness, independence, adventurousness, openness, knowledge, imagination, originality, sensitivity, fluency, flexibility, and accuracy were statistically meaningfully different compared to the book report group. Third, when it was analyzed how the convergence reading education program using Book-Coding affected the creative personality of the elementary school students, all the six factors of curiosity, task commitment, independence, awareness of risk, and openness of thinking, and aesthetics were found out to have a statistically more meaningful difference than the group that wrote a book report.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.133-140
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• 2022

We investigate the performance of deep learning-based Korean language models on a task of predicting the score range of Korean essays written by foreign students. We construct a data set containing a total of 304 essays, which include essays discussing the criteria for choosing a job ('job'), conditions of a happy life ('happ'), relationship between money and happiness ('econ'), and definition of success ('succ'). These essays were labeled according to four letter grades (A, B, C, and D), and a total of eleven essay score range prediction experiments were conducted (i.e., five for predicting the score range of 'job' essays, five for predicting the score range of 'happiness' essays, and one for predicting the score range of mixed topic essays). Three deep learning-based Korean language models, KoBERT, KcBERT, and KR-BERT, were fine-tuned using various training data. Moreover, two traditional probabilistic machine learning classifiers, naive Bayes and logistic regression, were also evaluated. Experiment results show that deep learning-based Korean language models performed better than the two traditional classifiers, with KR-BERT performing the best with 55.83% overall average prediction accuracy. A close second was KcBERT (55.77%) followed by KoBERT (54.91%). The performances of naive Bayes and logistic regression classifiers were 52.52% and 50.28% respectively. Due to the scarcity of training data and the imbalance in class distribution, the overall prediction performance was not high for all classifiers. Moreover, the classifiers' vocabulary did not explicitly capture the error features that were helpful in correctly grading the Korean essay. By overcoming these two limitations, we expect the score range prediction performance to improve.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.610-619
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• 2022

Hydrogen is emerging as an alternative fuel for eco-friendly ships because it reacts with oxygen to produce electrical energy and only water as a by-product. However, unlike regular fossil fuels, hydrogen has a material with a high risk of explosion due to its low ignition point and high flammability range. In order to safely use hydrogen in ships, it is an essential task to study the flow characteristics of hydrogen leakage and diffusion need to be studied. In this study, a numerical analysis was performed on the effect of leakage, ventilation, etc. on ventilation performance when hydrogen leaks in an enclosed space such as inside a ship. ANSYS CFX ver 18.1, a commercial CFD software, was used for numerical analysis. The leakage rate was changed to 1 q, 2 q, and 3 q at 1 q = 1 g/s, the ventilation rate was changed to 1 Q, 2 Q and 3 Q at 1 Q = 0.91 m/s, and the ventilation method was changed to type I, type II, type III to analyze the ventilation performance was analyzed. As the amount of leakage increased from 1 q to 3 q, the HMF in the storage room was about 2.4 to 3.0 times higher. Furthermore, the amount of ventilation to reduce the risk of explosion should be at least 2 Q, and it was established that type III was the most suitable method for the formation of negative pressure inside the hydrogen tank storage room.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.291-298
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• 2022

In this paper, we present a method for dependency parsing with chunking in Korean. Dependency parsing is a task of determining a governor of every word in a sentence. In general, we used to determine the syntactic governor in Korean and should transform the syntactic structure into semantic structure for further processing like semantic analysis in natural language processing. There is a notorious problem to determine whether syntactic or semantic governor. For example, the syntactic governor of the word "먹고 (eat)" in the sentence "밥을 먹고 싶다 (would like to eat)" is "싶다 (would like to)", which is an auxiliary verb and therefore can not be a semantic governor. In order to mitigate this somewhat, we propose a Korean dependency parsing after chunking, which is a process of segmenting a sentence into constituents. A constituent is a word or a group of words that function as a single unit within a dependency structure and is called a chunk in this paper. Compared to traditional dependency parsing, there are some advantage of the proposed method: (1) The number of input units in parsing can be reduced and then the parsing speed could be faster. (2) The effectiveness of parsing can be improved by considering the relation between two head words in chunks. Through experiments for Sejong dependency corpus, we have shown that the USA and LAS of the proposed method are 86.48% and 84.56%, respectively and the number of input units is reduced by about 22%p.