• Women's Health Nursing
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• v.18 no.1
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• pp.49-61
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• 2012

Purpose: The purpose of this study was to examine the experiences of the use of external breast prostheses among breast cancer survivors in Korea. Methods: A qualitative descriptive study was conducted, using focus groups. Data were collected from breast cancer survivors who were patients of C women's hospital in Seoul, Korea. Data were analyzed using content analysis in order to identify significant themes. Results: Participants included forty breast cancer survivors who had mastectomy as a surgical treatment. Four themes emerged from the collected data were: 1) concern over the high price of external breast prosthesis, 2) irregular use of external breast prosthesis, 3) unsatisfied with mastectomy bra, and 4) wanting to hide or not to talk about using breast prosthesis openly. Conclusion: Since most participants reported irregular use and negative experiences related to external breast prosthesis or mastectomy bra use, healthcare workers should allow more time for proper fitting and counseling and consulting with breast cancer survivors. In addition, health care providers as well as family and friends should keep in mind that cancer survivors need support that can help them cope by using positive reframing. Furthermore, improvements in the coverage of costs and services are needed for these women. This would be helpful for breast prosthesis users.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.4
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• pp.473-480
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• 2003

In order to control the genetic background noise in QTL mapping, cofactor markers were incorporated in single marker analysis (SMACO) and interval mapping (CIM). A simulation was performed to see how effective the cofactors were by the number of QTL, the number and the type of markers, and the marker spacing. The results of QTL mapping for the simulated data showed that the use of cofactors was slightly effective when detecting a single QTL. On the other hand, a considerable improvement was observed when dealing with more than one QTL. Genetic background noise was efficiently absorbed with linked markers rather than unlinked markers. Furthermore, the efficiency was different in QTL mapping depending on the type of linked markers. Well-chosen markers in both SMACO and CIM made the range of linkage position for a significant QTL narrow and the estimates of QTL effects accurate. Generally, 3 to 5 cofactors offered accurate results. Over-fitting was a problem with many regressor variables when the heritability was small. Various marker spacing from 4 to 20 cM did not change greatly the detection of multiple QTLs, but they were less efficient when the marker spacing exceeded 30 cM. Likelihood ratio increased with a large heritability, and the threshold heritability for QTL detection was between 0.30 and 0.05.

• 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.

• Earthquakes and Structures
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• v.10 no.2
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• pp.429-450
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• 2016

An attempt has been made to incorporate the concept of collapse safety margin into the procedures proposed in the performance-based earthquake engineering (PBEE) framework for direct earthquake loss estimation, in which the collapse probability curve obtained from incremental dynamic analysis (IDA) is mathematically characterized with the S-type fitting model. The regressive collapse probability curve is then used to identify non-collapse cases and collapse cases. With the assumed lognormal probability distribution for non-collapse damage indexes, the expected direct earthquake loss ratio is calculated from the weighted average over several damage states for non-collapse cases. Collapse safety margin is shown to be strongly related with sustained damage endurance of structures. Such endurance exhibits a strong link with expected direct earthquake loss. The results from the case study on three concrete frames indicate that increase in cross section cannot always achieve a more desirable output of collapse safety margin and less direct earthquake loss. It is a more effective way to acquire wider collapse safety margin and less direct earthquake loss through proper enhancement of reinforcement in structural components. Interestingly, total expected direct earthquake loss ratio seems to be insensitive a change in cross section. It has demonstrated a consistent correlation with collapse safety margin. The results also indicates that, if direct economic loss is seriously concerned, it is of much significance to reduce the probability of occurrence of moderate and even severe damage, as well as the probability of structural collapse.

• Korean Journal of Optics and Photonics
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• v.20 no.1
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• pp.48-52
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• 2009

We make a simple electrically controllable linear polarization rotator over $360^{\circ}$ without loss by using a thick ECB(electrically controlled birefringence) liquid crystal cell and a quarter wave plate at 514.5 nm wavelength. Its operating principle can be analyzed and explained by using simple polarization analysis and experimental data. We demonstrate that the degree of polarization of the rotator is 0.964 and the temporal variation for 1 week lies within ${\pm}1$ degree. We can easily solve the problem of nonlinearity of the dependence of the rotational angle of linear polarization on the applied voltage by changing the utilized voltage range or its fitting curve.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.2
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• pp.366-385
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• 2013

In this paper, we propose a compressed sensing (CS) based Wyner-Ziv (WZ) codec using motion-aligned auto regressive model (MAAR) based side information (SI) extrapolation to improve the compression performance of low-delay distributed video coding (DVC). In the CS based WZ codec, the WZ frame is divided into small blocks and CS measurements of each block are acquired at the encoder, and a specific CS reconstruction algorithm is proposed to correct errors in the SI using CS measurements at the decoder. In order to generate high quality SI, a MAAR model is introduced to improve the inaccurate motion field in auto regressive (AR) model, and the Tikhonov regularization on MAAR coefficients and overlapped block based interpolation are performed to reduce block effects and errors from over-fitting. Simulation experiments show that our proposed CS based WZ codec associated with MAAR based SI generation achieves better results compared to other SI extrapolation methods.

• 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1482-1489
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• 2007

In this paper, characteristics of infrasound and low-frequency noise emission from large modern wind turbines are experimentally investigated. The sound measurement procedures of IEC 61400-11 and ISO 7196 are utilized to field test and evaluation of noise emission from each of 1.5 MW and 660 kW wind turbines using the stall regulation and the pitch control for the power regulation, respectively. It was found that the G-weighted SPLs of low-frequency noise including infrasound shows positive correlation with the wind speeds, irrespective of methods of power regulation. This highlights the potential complaint of local community against the infrasound and low-frequency noise of wind turbines. The comparison of measured data with the existing hearing thresholds and criteria curves shows that it is highly probable that the low-frequency noise from the 1.5 MW and 660 kW wind turbines in the frequency range over 30 Hz leads to the psychological complaint of ordinary adults, and that the infrasound in the frequency range from 5 Hz to 8 Hz causes the complaint by rattling the house fitting such as doors and windows.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.8
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• pp.132-141
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• 2009

In this paper, we describe finger force estimation from surface electromyogram (sEMG) data for intuitive and delicate force control of robotic devices such as exoskeletons and robotic prostheses. Four myoelectric sites on the skin were found to offer favorable sEMG recording conditions. An artificial neural network (ANN) was implemented to map the sEMG to the force, and its structure was optimized to avoid both under- and over-fitting problems. The resulting network was tested using recorded sEMG signals from the selected myoelectric sites of three subjects in real-time. In addition, we discussed performance of force estimation results related to the length of the muscles. This work may prove useful in relaying natural and delicate commands to artificial devices that may be attached to the human body or deployed remotely.