• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.105-113
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• 2014

In this paper, we propose an OWL DL mapping rules for construction legal ontology based on the analyzed relationship between the structural features and elements of the statute. The mapping rule to be proposed is the method building the structure of the domestic statute, unique attribute of the statute, and reference relation between laws with TBox, and the legal sentence is analyzed, and the pattern type of the sentence is selected. It expresses with ABox. The proposed mapping rule is transformed to the information in which the computer can process the domestic legal document. It is usable for the legal knowledge base.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.338-347
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• 2018

This writing presents a structural methodology that conceptualize' Fransis Ford Coppola's perspective and adaptation style of his screenplay derived from the author Mario Puzo's original novel "The Godfather". This paper is divided into two different stages: The first stage, namely the "formal stage", encapsulates the analysis of narration and exploring the effects of structural transformations, such as deleting, creating and modifying from the novel to the screenplay. The second stage, entitled "semantic stage", is concerned with ascertaining the difference between the structure itself and the adapter's perspective on the original novel. The opening sequence of will be used as a case study to explore these issues in examining its adaptation style specifically while also offering a template for adapting a screenplay from a novel.

• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.35-42
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• 2019

This purpose of this study was to identify the meaning and structure of the experiences of women who were most stressed when they live and how they respond to stress when they were stressed.Six Female college students aged 29 years were interviewed for a total of three times until data was saturated and collected until no more data were collected. Interview data was processed through the analysis and interpretation process using the Giorgi phenomenological research method. As a result, 45 semantic units was derived, these were divided again into 12 subcomponents, and then divided into 3 categories. The stress situation of female college students was a way to escape from the stress that arises in situations that were difficult to overcome due to their inability to cope with, and that they were trying to take a walk or listen to music. A young women aged group, female college students becomes the center of the future family. Therefore, their emotional health will be more important than any other class. In conclusion, this study suggests the provision of a young women's tailed program that could help the female college students to experience the stress in the life task positively and to help the stress crisis as a positive experience of life.

• Journal of the Korean Society of Visualization
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• v.20 no.2
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• pp.78-85
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• 2022

In these days, the rapid development in prediction technology using artificial intelligent is being applied in a variety of engineering fields. Especially, dimensionality reduction technologies such as autoencoder and convolutional neural network have enabled the classification and regression of high-dimensional data. In particular, pixel level prediction technology enables semantic segmentation (fine-grained classification), or physical value prediction for each pixel such as depth or surface normal estimation. In this study, the pressure distribution of the ship's surface was estimated at the pixel level based on the artificial neural network. First, a potential flow analysis was performed on the hull form data generated by transforming the baseline hull form data to construct 429 datasets for learning. Thereafter, a neural network with a U-shape structure was configured to learn the pressure value at the node position of the pretreated hull form. As a result, for the hull form included in training set, it was confirmed that the neural network can make a good prediction for pressure distribution. But in case of container ship, which is not included and have different characteristics, the network couldn't give a reasonable result.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.289-303
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• 2022

In this study, the impact of assigned pixel labels on the accuracy of crack image identification of steel structures is examined by using an atrous separable convolution neural network (ASCNN). Firstly, images containing fatigue cracks collected from steel structures are classified into four datasets by assigning different pixel labels based on image features. Secondly, the DeepLab v3+ algorithm is used to determine optimal parameters of the ASCNN model by maximizing the average mean-intersection-over-union (mIoU) metric of the datasets. Thirdly, the ASCNN model is trained for various image sizes and hyper-parameters, such as the learning rule, learning rate, and epoch. The optimal parameters of the ASCNN model are determined based on the average mIoU metric. Finally, the trained ASCNN model is evaluated by using 10% untrained images. The result shows that the ASCNN model can segment cracks and other objects in the captured images with an average mIoU of 0.716.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.801-808
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• 2022

Research on how to embed knowledge in large-scale Linked Data and apply neural network models for entity matching is relatively scarce. The most fundamental problem with this is that different labels lead to lexical heterogeneity. In this paper, we propose an extended GCN (Graph Convolutional Network) model that combines re-align structure to solve this lexical heterogeneity problem. The proposed model improved the performance by 53% and 40%, respectively, compared to the existing embedded-based MTransE and BootEA models, and improved the performance by 5.1% compared to the GCN-based RDGCN model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.111-117
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• 2022

Many studies have been conducted on software fault prediction models for decades, and the models using machine learning techniques showed the best performance. Deep learning techniques have become the most popular in the field of machine learning, but few studies have used them as classifiers for fault prediction models. Some studies have used deep learning to obtain semantic information from the model input source code or syntactic data. In this paper, we produced several models by changing the model structure and hyperparameters using MLP with three or more hidden layers. As a result of the model evaluation experiment, the MLP-based deep learning models showed similar performance to the existing models in terms of Accuracy, but significantly better in AUC. It also outperformed another deep learning model, the CNN model.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.205-210
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• 2009

This paper proposes an ontology-based context aware system model for the purpose of storing and managing agricultural products using ubiquitous sensors to share and distribute information. In these days, according to penetrating ubiquitous technologies into our way of life, the importance of information is increasing gradually. The importance of ontology in a domain is getting as well. Therefore, this paper designs and build an ontology-based agricultural products warehouse model using context aware state information obtained by using wireless sensors. Also, it shows the result described by graphical ontology results to share common understanding on the structure of context information among users, devices and services to enable semantic interoperability owing to the information of the context aware state of the warehouse.

• Journal of Korea Trade
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• v.26 no.8
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• pp.37-57
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• 2022

Purpose -International diplomacy is key for the cohesive economic growth of countries around the world. This study aims to identify the major topics discussed and make sense of word pairs used in sentences by Chinese senior leaders during their diplomatic visits. It also compares the differences between key topics addressed during diplomatic visits to developed and developing countries. Design/methodology - We employed three methods: word frequency, co-word, and semantic network analysis. Text data are crawling state and official visit news released by the Ministry of Foreign Affairs of the People's Republic of China regarding diplomatic visits undertaken from 2015-2019. Findings - The results show economic and diplomatic relations most prominently during state and official visits. The discussion topics were classified according to nine centrality keywords most central to the structure and had the maximum influence in China. Moreover, the results showed that China's diplomatic issues and strategies differ between developed and developing countries. The topics mentioned in developing countries were more diverse. Originality/value - Our study proposes an effective approach to identify key topics in Chinese diplomatic talks with other countries. Moreover, it shows that discussion topics differ for developed and developing countries. The findings of this research can help researchers conduct empirical studies on diplomacy relationships and extend our method to other countries. Additionally, it can significantly help key policymakers gain insights into negotiations and establish a good diplomatic relationship with China.

• Journal of English Language & Literature
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• v.55 no.1
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• pp.129-152
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• 2009

The paper aims to prove symmetries of the left and right edges of a morphological unit through the analysis of English blends. Blends are the words derived from two existing words by snipping the middle parts out and putting the rest together, such as smog (