• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.98-107
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• 2018

In this paper, we propose a method to classify the skeletal maturity with a small amount of hand wrist X-ray image using deep learning-based meta-learning. General deep-learning techniques require large amounts of data, but in many cases, these data sets are not available for practical application. Lack of learning data is usually solved through transfer learning using pre-trained models with large data sets. However, transfer learning performance may be degraded due to over fitting for unknown new task with small data, which results in poor generalization capability. In addition, medical images require high cost resources such as a professional manpower and mcuh time to obtain labeled data. Therefore, in this paper, we use meta-learning that can classify using only a small amount of new data by pre-trained models trained with various learning tasks. First, we train the meta-model by using a separate data set composed of various learning tasks. The network learns to classify the bone maturity using the bone maturity data composed of the radiographs of the wrist. Then, we compare the results of the classification using the conventional learning algorithm with the results of the meta learning by the same number of learning data sets.

• Journal of Fashion Business
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• v.18 no.6
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• pp.116-136
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• 2014

The purpose of this study was to select patterns for slim fit pants, for the following main research, to develop new pants patterns that are suitable and preferable for men in their 20s. We compared and analyzed the patterns of which are currently in the market. We compared 10 different slim pants pattern drafting and analyzed their differences. Then, we examined their appearances and functionalities thru a male model test fitting 10 different samples of the pants. The conclusions of the research results were as follows. We listed the patterns in the following order based on the numbers of items each pattern has, which are statistically considerable for the evaluation to the optimum satisfactory level among the total of 35 testing categories: J > B=I > F=H > A > C=G > D > E. In the functionality test of the pants, we found that it was too tight around the waist and abdomen area with Pattern D, where-as it was too loose around the waist with Pattern C:,-, yet, both of the patterns indicated that it is a good fit in over-all. Therefore, we chose Pattern E, D, C, and G as the existing pants patterns that could be used for further research and for educational purposes to develop a slim pants pattern for men in their 20s.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.423-430
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• 2017

Recently, data analysis research has been carried out using the deep learning technique in various fields such as image interpretation and/or classification. Various types of algorithms are being developed for many applications. In this paper, we propose a precipitation prediction algorithm based on deep learning with high accuracy in order to take care of the possible severe damage caused by climate change. Since the geographical and seasonal characteristics of Korea are clearly distinct, the meteorological factors have repetitive patterns in a time series. Since the LSTM (Long Short-Term Memory) is a powerful algorithm for consecutive data, it was used to predict precipitation in this study. For the numerical test, we calculated the PWV (Precipitable Water Vapor) based on the tropospheric delay of the GNSS (Global Navigation Satellite System) signals, and then applied the deep learning technique to the precipitation prediction. The GNSS data was processed by scientific software with the troposphere model of Saastamoinen and the Niell mapping function. The RMSE (Root Mean Squared Error) of the precipitation prediction based on LSTM performs better than that of ANN (Artificial Neural Network). By adding GNSS-based PWV as a feature, the over-fitting that is a latent problem of deep learning was prevented considerably as discussed in this study.

• Korean Journal of Veterinary Research
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• v.43 no.3
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• pp.333-338
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• 2003

Cytogenetic and hematological analysis was performed in bovine peripheral blood from the regions around Wolsong nuclear power plant and control area. The frequency of micronuclei (MN) in peripheral blood lymphocytes from cattle was used as a biomarker of radiobiological effects resulting from exposure to environmental radiation. An estimated dare of radiation was calculated by a best fitting linear-quadratic model based on the radiation-induced MN formation from the bovine lymphocytes exposed in vitro to radiation over the range from 0 Gy to 4 Gy. MN rates in lymphocytes of cattle from Wolsong nuclear power plant and control area were 9.87/1,000 and 9.60/1,000, respectively. There were no significant differences in MN frequencies and hematological values in cattle between Wolsong and control area. The study indicates that the MN assay is a rapid, sensitive and accurate method that can be used to monitor a large population exposed to radiation.

• Fashion & Textile Research Journal
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• v.13 no.4
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• pp.624-634
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• 2011

The purpose of this study is to develop the pattern of safety clothes used at flat or mountainous areas and to identify the pattern of safety clothes by conducting experimental evaluation of virtual wear. Three subjects were selected, based on fire fighters' physical constitution. A prototype design for safety clothing was determined after in-depth interviewing of professionals and surveying of Forest service staff and related agency. Wearing test should be carried out in the order of pattern making, virtual and real wearing evaluation. For data analysis, technical statistical values should be obtained by using body measurements of subject, frequency analysis and T-test. The jacket is designed to have a front extension and the entire length of clothing enough for wearer to put on it over ordinary shirts or sweater. The collar of jacket is of round type. Cyber reality enables to identify the movement and activity of virtual fitting model and to find out errors or problems in safety clothing prior to on-the-spot wear test, thus raising the precision level of pattern. There was significant difference between real and virtual fit preference. The results show that the virtual try-on system need the development of a specific style.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.435-446
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• 2020

With the ability to learn rules from training data, the machine learning model can classify unknown objects. At the same time, the dimension of hyperspectral data is usually large, which may cause an over-fitting problem. In this research, an identification methodology of tea diseases was proposed based on spectral reflectance and machine learning, including the feature selector based on the decision tree and the tea disease recognizer based on random forest. The proposed identification methodology was evaluated through experiments. The experimental results showed that the recall rate and the F1 score were significantly improved by the proposed methodology in the identification accuracy of tea disease, with average values of 15%, 7%, and 11%, respectively. Therefore, the proposed identification methodology could make relatively better feature selection and learn from high dimensional data so as to achieve the non-destructive and efficient identification of different tea diseases. This research provides a new idea for the feature selection of high dimensional data and the non-destructive identification of crop diseases.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.163-172
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• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.63.1-63.1
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• 2011

To analyze the phenomenon of corrosion in the PV module, we experimented damp heat test at $85^{\circ}C$/85% relative humidity(RH) and $65^{\circ}C$/85% RH for 2,000 hours, respectively. We used 30 mini-modules designed of 6inch one cell. Despite of 2,000 hours test, measured $P_{max}$ is not reached failure which is defined less than 80% compared to initial $P_{max}$. Therefore, we calculate proper curve fitting over 2,000 hours. Data less than 80% $P_{max}$ is found and B10 lifetime is calculated by the number of failure specimens and weibull distribution. Using B10 lifetime that the point of failure rate 10% and Peck's model, the predictable equation of lifetime was derived under temperature and humidity condition.

• Earthquakes and Structures
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• v.25 no.6
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• pp.443-454
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• 2023

Steel shear walls are used to strengthen steel and concrete structures. One such system is Partial Attached Steel Shear Walls (PASSW), which are only connected to frame beams. This system offers both structural and architectural advantages. This study first calibrated the numerical model of RC frames with and without PASSW using an experimental sample. The seismic performance of the RC frame was evaluated by 30 non-linear static analyses, which considered stiffness, ductility, lateral strength, and energy dissipation, to investigate the effect of PASSW width and column axial load. Based on numerical results and a curve fitting technique, a lateral stiffness equation was developed for frames equipped with PASSW. The effect of the shear wall location on the concrete frame was evaluated through eight analyses. Nonlinear dynamic analysis was performed to investigate the effect of the shear wall on maximum frame displacement using three earthquake records. The results revealed that if PASSW is designed with appropriate stiffness, it can increase the energy dissipation and ductility of the frame by 2 and 1.2 times, respectively. The stiffness and strength of the frame are greatly influenced by PASSW, while axial force has the most significant negative impact on energy dissipation. Furthermore, the location of PASSW does not affect the frame's behavior, and it is possible to have large openings in the frame bay.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.441-452
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• 2013

This study analyzed the relationship between key film and a box office record success factors based on movies released in the first quarter of 2013 in Korea. An over-fitting problem can happen if there are too many explanatory variables inserted to regression model; in addition, there is a risk that the estimator is instable when there is multi-collinearity among the explanatory variables. For this reason, optimal variable selection based on high explanatory variables in box-office performance is of importance. Among the numerous ways to select variables, LASSO estimation applied by a generalized linear model has the smallest prediction error that can efficiently and quickly find variables with the highest explanatory power to box-office performance in order.