• Kyungpook Mathematical Journal
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• v.64 no.2
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• pp.311-335
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• 2024

The fundamental quandle is a powerful invariant of knots, links and spatial graphs, but it is often difficult to determine whether two quandles are isomorphic. One approach is to look at quotients of the quandle, such as the n-quandle defined by Joyce [8]; in particular, Hoste and Shanahan [5] classified the knots and links with finite n-quandles. Mellor and Smith [12] introduced the N-quandle of a link as a generalization of Joyce's n-quandle, and proposed a classification of the links with finite N-quandles. We generalize the N-quandle to spatial graphs, and investigate which spatial graphs have finite N-quandles. We prove basic results about N-quandles for spatial graphs, and conjecture a classification of spatial graphs with finite N-quandles, extending the conjecture for links in [12]. We verify the conjecture in several cases, and also present a possible counterexample.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.237-251
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• 2023

This study explores the possibility of automated scoring for scientific graph answers by designing an automated scoring model using convolutional neural networks and applying it to students' kinematics graph answers. The researchers prepared 2,200 answers, which were divided into 2,000 training data and 200 validation data. Additionally, 202 student answers were divided into 100 training data and 102 test data. First, in the process of designing an automated scoring model and validating its performance, the automated scoring model was optimized for graph image classification using the answer dataset prepared by the researchers. Next, the automated scoring model was trained using various types of training datasets, and it was used to score the student test dataset. The performance of the automated scoring model has been improved as the amount of training data increased in amount and diversity. Finally, compared to human scoring, the accuracy was 97.06%, the kappa coefficient was 0.957, and the weighted kappa coefficient was 0.968. On the other hand, in the case of answer types that were not included in the training data, the s coring was almos t identical among human s corers however, the automated scoring model performed inaccurately.

• Tunnel and Underground Space
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• v.17 no.6
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• pp.503-510
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• 2007

Rock classification schemes such as RMR, Q-system were applied to investigate the stability of large-scale gangways mined in a limestone mine. 22 areas for engineering geological surveys were selected and rock classifications at each survey point had been carried out. Considering the fact that the observed gangways have not experienced some severe failure and have been stably maintained till now, it is found that Q-system is more reasonable than RMR in evaluating the stability of unsupported span. Also, extended Mathews stability graph method which is a kind of revised Q-system was used to assess the stability of gangways and the results represent that all gangways except for one area are under stable condition. Based on above the mentioned results, the empirical equations to design the maximum unsupported span and critical height of a large-scale gangway are suggested.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.309-320
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• 2020

Bayesian networks, also known as directed acyclic graphs (DAG), are used in many areas of medicine, meteorology, and genetics because relationships between variables can be modeled with graphs and probabilities. In particular, Bayesian network classifiers, which are used to predict discrete data, have recently become a new method of data mining. Bayesian networks can be grouped into different models that depend on structured learning methods. In this study, Bayesian network models are learned with various properties of structure learning. The models are compared to the simplest method, the naïve Bayes model. Classification results are compared by applying learned models to various real data. This study also compares the relationships between variables in the data through graphs that appear in each model.

• Smart Media Journal
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• v.10 no.1
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• pp.32-38
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• 2021

Image classification through deep learning on the image from photographs has been a very active research field for the past several years. In this paper, we propose a method of automatically discriminating stone images from domestic source through deep learning, which is to use Python's hash library to scan 300×300 pixel photo images of granites such as Hwangdeungseok, Goheungseok, and Pocheonseok, performing data preprocessing to create learning images by examining duplicate images for each stone, removing duplicate images with the same hash value as a result of the inspection, and deep learning by stone. In addition, to utilize VGGNet, the size of the images for each stone is resized to 224×224 pixels, learned in VGG16 where the ratio of training and verification data for learning is 80% versus 20%. After training of deep learning, the loss function graph and the accuracy graph were generated, and the prediction results of the deep learning model were output for the three kinds of stone images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.34-36
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• 2022

In the existing text sentiment analysis models, the entire text is usually directly modeled as a whole, and the hierarchical relationship between text contents is less considered. However, in the practice of sentiment analysis, many texts are mixed with multiple emotions. If the semantic modeling of the whole is directly performed, it may increase the difficulty of the sentiment analysis model to judge the sentiment, making the model difficult to apply to the classification of mixed-sentiment sentences. Therefore, this paper proposes a sentiment analysis model BHGCN that considers the text hierarchy. In this model, the output of hidden states of each layer of BERT is used as a node, and a directed connection is made between the upper and lower layers to construct a graph network with a semantic hierarchy. The model not only pays attention to layer-by-layer semantics, but also pays attention to hierarchical relationships. Suitable for handling mixed sentiment classification tasks. The comparative experimental results show that the BHGCN model exhibits obvious competitive advantages.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1031-1040
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• 2004

Electrocardiogram being the recording of the heart's electrical activity provides valuable clinical information about heart's status. Many re-searches have been pursued for heart disease diagnosis using ECG so far. However, electrocardio-graph uses foreign diagnosis algorithm due to inaccuracy of diagnosis results for a heart disease. This paper suggests ECG data collection, data preprocessing and heart disease pattern classification using data mining. This classification technique is the FB(Frequent pattern Bayesian) classifier and is a combination of two data mining problems, naive bayesian and frequent pattern mining. FB uses Product Approximation construction that uses the discovered frequent patterns. Therefore, this method overcomes weakness of naive bayesian which makes the assumption of class conditional independence.

• Journal of Internet Computing and Services
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• v.2 no.3
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• pp.37-49
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• 2001

Korean auxiliary verbs are often used in Korean sentences in spite of the small number of the auxiliary verbs, However. the incorrect processing of the verbs concept leads to the poor translation quality. To solve the problems of the auxiliary verb processing. the paper proposes a description of the auxiliary verbs based on Conceptual Graph (CG), For the description of the auxiliary verbs within CG. we first collect 40 Korean auxiliary verbs and example sentences from papers and a Korean dictionary, Next, we perform the analysis of the Korean auxiliary verbs through a classification: perfective, progressive, benefactive, attemptive, emphatic, desirable, retentive, and presumptive. Then we depict the eight meanings based on CG. In the experiment. the paper implements the program that translates sentences included in the auxiliary verbs into CG and explains the experimental results.

• Tunnel and Underground Space
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• v.22 no.5
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• pp.297-309
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• 2012

The stability assessment of abandoned gangway for the purpose of services was performed. Among the many factors that affect the stability of openings, the span of the opening in a given rock mass condition provides an important element of design. In this paper, the stability of gangway was assessed by the critical span curves proposed by Lang, the modified Mathews'stability graph method and using support measures of the Q system. In the evaluation of stability as a whole the gangway is considered as stable. But the rockfalls of wedge-shaped blocks were expected in the area in which the horizontal joints of low angle appear. The support measures such as local rock bolts are required to use for commercial purposes of the abandoned gangway. And entrance section may require the particular attention as unstable section. Since there are so many spalling due to bad blasting in the roof and sidewall of gangway, the scaling operations should be followed primarily.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.656-662
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• 2012

In this paper, we describe a new summarizing method based on a graph-based and a sense-based analysis. In the graph-based analysis, we convert sentences in a document into word vectors and calculate the similarity between each sentence using LSA. We reflect this similarity of sentences and the rarity scores of words in sentences to define weights of edges in the graph. Meanwhile, in the sense-based analysis, in order to determine the sense of words, subjectivity or objectivity, we built a database which is extended from the golden standards using Wordnet. We calculate the subjectivity of sentences from the sense of words, and select more subjective sentences. Lastly, we combine the results of these two methods. We evaluate the performance of the proposed method using classification games, which are usually used to measure the performances of summarization methods. We compare our method with the MS-Word auto-summarization, and verify the effectiveness of ours.