• Archives of Plastic Surgery
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• v.48 no.1
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• pp.44-48
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• 2021

Previously reported nipple-areolar complex reconstruction (NAR) methods involve multiple incisions and wide skin redraping, which increase retraction forces and heighten the risk of nipple-areolar complex (NAC) flattening. We introduce a NAR method using the long V-Y advancement technique that can overcome these disadvantages. A V-shaped flap is designed with the width of the flap base 4-5 mm larger than the diameter of the normal nipple. The flap length is designed to be at least 2.5 times its width. Dissection is performed to the top of the artificial dermal matrix or muscle layer. The nipple is constructed with the same projection as the contralateral side by folding the elevated flap. The tip of the elevated flap is apposed in the middle of the donor defect to minimize the deformity during donor site closure. A 3-point skin suture is applied to the upper third of the folded flap to mold its shape. Using this long V-Y advancement technique, we successfully decreased skin tension in NAC flaps and improved the maintenance of reconstructed nipple projection. The long V-Y advancement technique provides an easy, simple NAR method, effectively maintaining longer nipple projections and reducing breast deformities, especially in Asian women with relatively large nipples.

• Journal of Korean Port Research
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• v.12 no.1
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• pp.9-17
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• 1998

The evaluation structure of complex problems is composed of multi-attributes and hierarchy. A many studies were existed on this problems, but that based on the assumption that the evaluation elements were independent. The actual evaluation problems have the complexity, ambiguity and interlinkage among the elements. In this situation, the fuzzy evaluation process is very effective in settling the complex problems. For evaluation of large scale hierarchical MADM problem, the fuzzy evaluation algorithm is developed in this paper, and that is centering on the identification of fuzzy measures. In this study, we newly identified the weight and interaction among the evaluation attributes. The results of this study are as follows: we can identified the hierarchical structure of the evaluation problem which is composed of the evaluation structure, function and hierarchy; we improved the existed weighting method which could be accomplished by normalizing process, considering the uncertainty and new weight integrating method which come from Dempster-Shafer theory. And we take into account the interaction properties among more than 3 evaluation attributes, which can be compared with the existed studies in which only 2 evaluation attributes taked into account.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.56-66
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• 2023

The goal of deep learning is to extract complex features from multidimensional data use the features to create models that connect input and output. Deep learning is a process of learning nonlinear features and functions from complex data, and the user data that is employed to train deep learning models has become the focus of privacy concerns. Companies that collect user's sensitive personal information, such as users' images and voices, own this data for indefinite period of times. Users cannot delete their personal information, and they cannot limit the purposes for which the data is used. The study has designed a deep learning method that employs privacy protection technology that uses distributed collaborative learning so that multiple participants can use neural network models collaboratively without sharing the input datasets. To prevent direct leaks of personal information, participants are not shown the training datasets during the model training process, unlike traditional deep learning so that the personal information in the data can be protected. The study used a method that can selectively share subsets via an optimization algorithm that is based on modified distributed stochastic gradient descent, and the result showed that it was possible to learn with improved learning accuracy while protecting personal information.

• Journal of Climate Change Research
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• v.6 no.4
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• pp.331-344
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• 2015

A long-term gridded historical data at 3 km spatial resolution has been generated for practical regional applications such as hydrologic modelling. However, overly high or low values have been found at some grid points where complex topography or sparse observational network exist. In this study, the Inverse Distance Weighting (IDW) method was applied to properly smooth the overly predicted values of Improved GIS-based Regression Model (IGISRM), called the IDW-IGISRM grid data, at the same resolution for daily precipitation, maximum temperature and minimum temperature from 2001 to 2010 over South Korea. We tested various effective distances in the IDW method to detect an optimal distance that provides the highest performance. IDW-IGISRM was compared with IGISRM to evaluate the effectiveness of IDW-IGISRM with regard to spatial patterns, and quantitative performance metrics over 243 AWS observational points and four selected stations showing the largest biases. Regarding the spatial pattern, IDW-IGISRM reduced irrational overly predicted values, i. e. producing smoother spatial maps that IGISRM for all variables. In addition, all quantitative performance metrics were improved by IDW-IGISRM; correlation coefficient (CC), Index Of Agreement (IOA) increase up to 11.2% and 2.0%, respectively. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were also reduced up to 5.4% and 15.2% respectively. At the selected four stations, this study demonstrated that the improvement was more considerable. These results indicate that IDW-IGISRM can improve the predictive performance of IGISRM, consequently providing more reliable high-resolution gridded data for assessment, adaptation, and vulnerability studies of climate change impacts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1800-1817
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• 2022

With the development of the economy and the improvement of living standards, the hot issues in the subject area have become the main research direction, and the mining of the hot issues in the subject currently has problems such as a large amount of data and a complex algorithm structure. Therefore, in response to this problem, this study proposes a method for extracting hot keywords in scientific journals based on the improved BERT model.It can also provide reference for researchers,and the research method improves the overall similarity measure of the ensemble,introducing compound keyword word density, combining word segmentation, word sense set distance, and density clustering to construct an improved BERT framework, establish a composite keyword heat analysis model based on I-BERT framework.Taking the 14420 articles published in 21 kinds of social science management periodicals collected by CNKI(China National Knowledge Infrastructure) in 2017-2019 as the experimental data, the superiority of the proposed method is verified by the data of word spacing, class spacing, extraction accuracy and recall of hot keywords. In the experimental process of this research, it can be found that the method proposed in this paper has a higher accuracy than other methods in extracting hot keywords, which can ensure the timeliness and accuracy of scientific journals in capturing hot topics in the discipline, and finally pass Use information technology to master popular key words.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.505-512
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• 2008

Over the past years, complex project which has the object to accomplish housing complex, commercial complex etc, recently is changing to multi-complex projects because of development in IT sector. Improvement in the quality of life, life pattern has been and is being changed, these modernized and improved version of life brings the concept of U-city. Department of urban planning and engineering, civil engineering and architecture engineering in every university educate students according to the changing world in order to handle these complex projects in real world. In most cases department of urban planning and engineering teach project planning and department of civil and architecture engineering teach project design and construction. In most of the projects planning followed by design and construction need to be accomplished, but current observation in the present curriculum shows that it is difficult to expect the continuity. The present curriculum of civil engineering has to change as complex projects deal with various different structures during the design and construction phase of these projects. This study examined curriculums from the department of urban and civil engineering related to structural design and construction and survey importance of design works which overlap urban and civil engineering targeted on design engineers. After understanding design works and results obtained from survey we propose for an advanced efficiency method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.11 s.102
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• pp.1123-1130
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• 2005

In this paper, we propose an improved side information processing scheme which is important in the sub-block phase weighting(SPW) method for the peak-to-average power ratio(PAPR) reduction. SPW method is to divide the input OFDM subchannels into several subblocks and to multiply phase weighting with each subblocks, properly for the reduction of the peak power. SPW method is similar to the conventional PTS method when the number of sub-carriers, signal modulation format and the number of subblocks are the same. However, unlike the conventional PTS(Partial Transmit Sequence) and SLM(Selected Mapping) method using many stages of IFFT(Inverse Fast Fourier Transform), SPW method only needs one IFFT. Although PAPR can be reduced by SPW method, complex computation burden still remains. In this paper the flipping algorithm and the full iteration algorithm are used f3r the phase control method. Through the computer simulation, we analyze and discuss the properties and the performance of the suggested method.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.267-269
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• 2006

In this paper, a new single phase voltage sag/swell compensator using direct power conversion is introduced. A new compensator consists of input/output filter, series transformer and direct ac-ac converter, which is a single-phase back-to-back PWM converter without dc-link capacitors. Advantages of the proposed compensator include: simple power circuit by eliminating dc-link electrolytic capacitors and thereby, improved reliability and increased life time of the entire compensator; simple PWM strategy to compensate voltage sag/swell at the same time and reduced switching losses in the ac-ac converter. Further, the proposed scheme is able to adopt simple switch commutation method without requiring complex four-step commutation method commonly required in the direct power conversion. Simulation results are shown to demonstrate the advantages of the new compensator and PWM strategy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.122-129
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• 2010

High-performance concrete (HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance (MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.7
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• pp.1001-1007
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• 2014

This paper proposes a method to reduce operation time using discrete cosine transform and to improve image quality by the DC gain correction. Conventional filtered back projection (FBP) filtering in the frequency domain using Fourier transform, but the filtering process uses complex number operations. To simplify the filtering process, we propose a filtering process using discrete cosine transform. In addition, the image quality of reconstructed images are improved by correcting DC gain of sinograms. To correct the DC gain, we propose to find an optimum DC weight is defined as the ratio of sinogram DC and optimum DC. Experimental results show that the proposed method gets better performance than the conventional method for phantom and clinical CT images.