• Archives of Reconstructive Microsurgery
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• v.24 no.1
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• pp.32-36
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• 2015

The reconstruction of recurrent pressure sores is challenging due to a limited set of treatment options and a high risk of flap loss. Successful treatment requires scrupulous surgical planning and a multidisciplinary approach. Although the tensor fascia lata flap is regarded as the standard treatment of choice-it provides sufficient tissue bulk for a deep trochanteric sore defect-plastic surgeons must always consider the potential of recurrence and accordingly save the second-best tissues. With the various applications of anterolateral thigh (ALT) flaps in the reconstructive field, we report two cases wherein an alternative technique was applied, whereby pedicled ALT fasciocutaneous island flaps were used to cover recurrent trochanteric pressure sores. The postoperative course was uneventful without any complications. The flap provided a sound aesthetic result without causing a dog-ear formation or damaging the lower-leg contour. This flap was used as an alternative to myocutaneous flaps, as it can cover a large trochanteric defect, recurrence is minimized, and the local musculature and lower-leg contour are preserved.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of KIISE
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• v.44 no.5
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• pp.503-509
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• 2017

In this paper, the copy mechanism and input feeding are applied to recurrent neural network(RNN)-search model in a Korean-document summarization in an end-to-end manner. In addition, the performances of the document summarizations are compared according to the model and the tokenization format; accordingly, the syllable-unit, morpheme-unit, and hybrid-unit tokenization formats are compared. For the experiments, Internet newspaper articles were collected to construct a Korean-document summary data set (train set: 30291 documents; development set: 3786 documents; test set: 3705 documents). When the format was tokenized as the morpheme-unit, the models with the input feeding and the copy mechanism showed the highest performances of ROUGE-1 35.92, ROUGE-2 15.37, and ROUGE-L 29.45.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.7
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• pp.582-588
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• 2001

The Multiple Model/Controller IMC(MMC-IMC) is a model-based control method which uses a set of model/controller pairs rather than a single model/controller to handle all possible operating conditions in the IMC control structure. During operation, one model/controller pair that best fit, for current plant situation is chosen by the switching algorithm. The major drawback of the switching controller is the bad transient performance due to the model error and the use fo linear controller for nonlinear plants. In this paper, we propose a method that transient response of the MMC-IMC using two recurrent neural networks. Simulation result shows that the proposed method represents better performance than the usual MMC-IMC`s.

• Transactions of Materials Processing
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• v.33 no.5
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• pp.354-362
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• 2024

In this paper, the capabilities of recurrent neural networks (RNNs) to describe the viscoelastic properties of acrylonitrile-butadiene styrene (ABS) are investigated. The RNN model was trained using one-dimensional strains and corresponding stress data generated by the finite element method. The optimal model was then employed to predict the viscoelastic behavior of unseen test data. Furthermore, the viscoelastic-based RNN model was tested for extrapolation using other types of strain and corresponding stress data beyond the training set. The agreement between the predicted and actual stresses demonstrates the robust performance of the trained RNN model in predicting different types of strain inputs for larger strain tests, despite being trained only with step strain inputs. Therefore, the use of RNNs can be considered a viable alternative to conventional models for predicting viscoelastic behavior.

• Journal of Korea Multimedia Society
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• v.5 no.3
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• pp.281-289
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• 2002

Few studies have been systematically pursued on a multimedia data mining in despite of the over-whelming amounts of multimedia data by the development of computer capacity, storage technology and Internet. Based on the preliminary image processing and content-based image retrieval technology, this paper presents the methods for discovering association rules from recurrent items with spatial relationships in huge data repositories. Furthermore, multimedia mining algorithm is proposed to find implicit association rules among objects of which content-based descriptors such as color, texture, shape and etc. are recurrent and of which descriptors have spatial relationships. The algorithm with recurrent items in images shows high efficiency to find set of frequent items as compared to the Apriori algorithm. The multimedia association-rules algorithm is specially effective when the collection of images is homogeneous and it can be applied to many multimedia-related application fields.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

• Korean Journal of Mathematics
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• v.22 no.1
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• pp.207-216
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• 2014

The manifold $^*g-ESX_n$ is a generalized n-dimensional Riemannian manifold on which the differential geometric structure is imposed by the unified field tensor $^*g^{{\lambda}{\nu}}$ through the ES-connection which is both Einstein and semi-symmetric. The purpose of the present paper is to study the algebraic geometric structures of 3-dimensional $^*g-ESX_3$. Particularly, in 3-dimensional $^*g-ESX_3$, we derive a new set of powerful recurrence relations in the first class.

• Journal of the Korean Mathematical Society
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• v.38 no.3
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• pp.613-621
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• 2001

In this paper we show that if a dynamical system $\phi$ has bishadowing and cyclically bishadowing properties on the chain recurrent set CR($\phi$) then all nearby continuous perturbations of $\phi$ behave chaotically on a neighborhood of each chain component of $\phi$ wheneer it has a fixed point. This is a generalization of the results obtained by Diamond et al.([3]).

• Korean Journal of Mathematics
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• v.24 no.3
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• pp.319-330
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• 2016

The manifold $^*g-ESX_n$ is a generalized n-dimensional Riemannian manifold on which the differential geometric structure is imposed by the unied eld tensor $^*g^{{\lambda}{\nu}}$ through the ES-connection which is both Einstein and semi-symmetric. The purpose of the present paper is to study the algebraic geometric structures of 5-dimensional $^*g-ESX_5$. Particularly, in 5-dimensional $^*g-ESX_5$, we derive a new set of powerful recurrence relations in the first class.