• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.77-97
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• 2017

A recent sharp increase of the biomedical literature causes researchers to struggle to grasp the current research trends and conduct creative studies based on the previous results. In order to alleviate their difficulties in keeping up with the latest scholarly trends, numerous attempts have been made to develop specialized analytic services that can provide direct, intuitive and formalized scholarly information by using various text mining technologies such as information extraction and event detection. This paper introduces and evaluates total 8 Convolutional Neural Network (CNN) models for extracting biomedical events from academic abstracts by applying various feature utilization approaches. Also, this paper conducts performance comparison evaluation for the proposed models. As a result of the comparison, we confirmed that the Entity-Type-Fully-Connected model, one of the introduced models in the paper, showed the most promising performance (72.09% in F-score) in the event classification task while it achieved a relatively low but comparable result (21.81%) in the entire event extraction process due to the imbalance problem of the training collections and event identify model's low performance.

• Journal of Wetlands Research
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• v.15 no.2
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• pp.179-189
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• 2013

The Haean basin is a unique geographical feature formed by differential erosion and it borders the military demarcation line. Recently the basin has become an interest of civilians due to security tour, highland vegetables and wetland. After the civil war, the population decreased but it has increased since 2007. The annual mean air temperature in the basin has increased with a rate of $+0.016^{\circ}C/yr$ and the annual precipitation also has increased with a rate of +10.41 mm/yr. The precipitation occurring in June~August (wet season) occupied most of the total precipitation increase. In addition, recently the number of groundwater wells and its use have gradually increased and most of them are for agriculture including cultivation of rice and highland vegetables. If the air temperature further increases in the future according to the climate change scenarios, the highland vegetables cultivation will be difficult. Furthermore, if the rainstorm in the summer will be enforced, the groundwater recharge and water management will be aggravated. Therefore, an evaluation for sustainable groundwater development in the basin and a reform of the current agriculture (change of cultivating crops) depending on much water are essentially required.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.10
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• pp.1665-1672
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• 2009

This study examines the comfort of outdoorwear by electrocardiogram (ECG) and electroencephalogram (EEG) analyses. An experiment that consisted of rest (30 min), exercise (30 min), and recovery (20 min) periods was administered in a climate chamber with 10 healthy male participants. Two kinds of outdoorwear made of 100% cotton fabrics ('Control') and specially engineered fabrics having the feature of quick sweat absorbency and high speed drying fabric ('Functional') are evaluated in the experiment. ECG and EEG signals were obtained during the rest and recovery periods for the two outdoorwear conditions. The ECG analysis identified a smaller decrement of high frequency (HF) power for the 'Functional' when compared with the 'Control' during the recovery period. Next, the EEG analysis showed that the relative band powers of slow $\alpha$ and mid $\alpha$ increased for the 'Functional' while they decreased for the 'Control' and that the ratio of $\alpha$ power to high $\beta$ power was higher for the 'Functional'. The evaluation results indicate that the participants could remain relaxed more with less stress while wearing the functional outdoorwear that demonstrated the positive effects on autonomic nervous system (ANS) activities. The present study is significant in regard that use of ECG and EEG for the assessment of wear comfort is the first in the field of clothing and textile.

• The KIPS Transactions:PartC
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• v.11C no.2
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• pp.245-252
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• 2004

Stream Control Transmission Protocol(SCTP) is a new connection-oriented, reliable delivery transport protocol operating on top of an unreliable connectionless packet service such as IP. It inherits many of the functions developed for TCP, including flow control and packet loss recovery functions. In addition, it also supports transport layer multihoming and multistreaming In this paper, we study the impact of multi-homing on the performance of SCTP. We first compare performance of single-homed SCTP. multi-homed SCTP, TCP Reno and TCP SACK. We, then describe potential flaw in the current SCTP retransmission policy, when SCTP host is multihomed. Our Results show that SCTP performs better than TCP Reno and TCP SACK due to several changes from TCP in its congestion control mechanism. In particular. multi-homed SCTP shows the best result among the compared schemes. Through experimentation for multi-homed SCTP, we found that the current SCTP retransmission policy nay deteriorate the perfomance when the retransmission path it worse than the original path. Therefore, the condition of retransmission path is a very important factor In SCTP performance and a proper mechanism would be required to measure the condition of the retransmission path.

• Fashion & Textile Research Journal
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• v.15 no.6
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• pp.1010-1017
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• 2013

Current global textiles and fashion industries have gradually shifted focus to high value-added, high sensibility, and multi-functional products based on new human-friendliness and sustainable growth technologies. Textile design CAD systems have been developed in conjunction with computer hardware and software sector advances. This study compares the patterns or images of actual woven fabrics and virtual fabrics prepared with a textile design CAD system. In this study, several weave structures (such as fancy yarn weave and patterns) were prepared with a shuttle loom. The woven textile images were taken using a CCD camera. The same weave structure data and yarn data were fed into a textile design CAD system in order to simulate fabric images as similarly as possible. Similarity Index analysis methods allowed for an analysis of the index between the actual fabric specimen and the simulated image of the corresponding fabric. The results showed that repeated small pattern weaves provide superior similarity index values than those of a fancy yarn weave that indicate some irregularities due to fancy yarn attributes. A Complex Wavelet Structural Similarity(CW-SSIM) index resulted in a better index than other methods such as Multi-Scale(MS) SSIM, and Feature Similarity(FS) SSIM, across fabric specimen images. A correlation analysis of the similarity index based on an image analysis and a similarity evaluation by panel members was also implemented.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.8
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• pp.1456-1463
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• 2010

The safety critical systems in nuclear power plants should be designed to have a high level of fault tolerant capability because those systems are used for protection or mitigation of the postulated accidents of nuclear reactor. Due to increasing of the system complexity of the digital based system in nuclear fields, the reliability of the digital based systems without an auto-test or a self-diagnostic feature is generally lower than those of analog system. To overcome this problem, additional redundant architectures in each redundant channel and self-diagnostic features are commonly integrated into the digital safety systems. The self diagnostic function is a key factor for increasing fault tolerant capabilities in the digital based safety system. This paper presents an availability and safety evaluation model to analyze the effect to the system's fault tolerant capabilities depending on self-diagnostic features when the loss or erroneous behaviors of self-diagnostic function are expected to occur. The analysis result of the proposed model on the several modules of a safety platform shows that the improvement effect on unavailability of each module has generally become smaller than the result of usage of conventional models and the unavailability itself has changed significantly depending on the characteristics of failures or errors of self-diagnostic function.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.47-56
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• 2009

Effective detection and correction of outliers of raw traffic data collected from the field is of keen interest because reliable traffic information is highly dependent on the quality of raw data. Global positioning system (GPS) based traffic surveillance systems are capable of producing individual vehicle speeds that are invaluable for various traffic management and information strategies. This study proposed a locally weighted regression (LWR) based filtering method for individual vehicle speed data. An important feature of this study was to propose a technique to generate synthetic outliers for more systematic evaluation of the proposed method. It was identified by performance evaluations that the proposed LWR-based method outperformed an exponential smoothing. The proposed method is expected to be effectively utilized for filtering out raw individual vehicle speed data.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.4
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• pp.405-419
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• 2007

A series of tracer experiments for the evaluation of atmospheric dispersion was performed over the urban area of Seoul using two inert, non-deposition perfluorocarbon (PMCH and m-PDCH) gases during three years campaign on 2002, 2003 and 2005. 30 sampling sites for collecting these tracers were located along two arcs of 2.5 and 5 kilometers downwind from the release point. About ten measurements which each lasted for 2 hours or 4 hours were made over the two consecutive days during each campaign. CALPUFF and MM5 meteorological model were applied to evaluate the urban dispersion in detail. Size of Modeling domain was $27\;km{\times}23\;km$ and the fine nest in the modeling domain had a grid size of 0.5 km. The results showed that CALPUFF dispersion model had a tendency to estimate tracer concentrations about $2{\sim}5$ times less than those of ambient samples under many conditions. These consistent inaccuracy in urban dispersion was attributed to inherent inaccuracy and lack of details in terrain data at urban area.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.1
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• pp.41-51
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• 2017

This Paper Describes a Method for the Evaluation of Sleep Apnea, Namely, the Peak Signal-to-noise ratio (PSNR) of Wavelet Transformed Electroencephalography (EEG) Data. The Purpose of this Study was to Investigate EEG Properties with Regard to Differences between Sleep Spindles and K-complexes and to Characterize Obstructive Sleep Apnea According to Sleep Stage. We Examined Non-REM and REM Sleep in 20 Patients with OSA and Established a New Approach for Detecting Sleep Apnea Base on EEG Frequency Changes According to Sleep Stage During Sleep Apnea Events. For Frequency Bands Corresponding to A3 Decomposition with a Sampling Applied to the KC and the Sleep Spindle Signal. In this Paper, the KC and Sleep Spindle are Ccalculated using MSE and PSNR for 4 Types of Mother Wavelets. Wavelet Transform Coefficients Were Obtained Around Sleep Spindles in Order to Identify the Frequency Information that Changed During Obstructive Sleep Apnea. We also Investigated Whether Quantification Analysis of EEG During Sleep Apnea is Valuable for Analyzing Sleep Spindles and The K-complexes in Patients. First, Decomposition of the EEG Signal from Feature Data was Carried out using 4 Different Types of Wavelets, Namely, Daubechies 3, Symlet 4, Biorthogonal 2.8, and Coiflet 3. We Compared the PSNR Accuracy for Each Wavelet Function and Found that Mother Wavelets Daubechies 3 and Biorthogonal 2.8 Surpassed the other Wavelet Functions in Performance. We have Attempted to Improve the Computing Efficiency as it Selects the most Suitable Wavelet Function that can be used for Sleep Spindle, K-complex Signal Processing Efficiently and Accurate Decision with Lesser Computational Time.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.3
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• pp.313-322
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• 2005

Rapid advances in technology and changes in human and cultural activities bring about changes to the earth surface in terms of spatial extension as well as time frame of the changes. Such advances introduce shorter updating frequency of maps and geospatial database. To satisfy these requirements, recent research efforts in the geoinformatics field have been focused on the automation and speeding up of the mapping processes which resulted in products such as the digital photogrammetric workstation, GPSIINS, applications of satellite imagery, automatic feature extraction and the LiDAR system. The possibility of automatically extracting buildings and generating contours from airborne LiDAR data has received much attention because LiDAR data produce promising results. However, compared with the manually derived building footprints using traditional photogrammetric process, more investigation and analysis need to be carried out in terms of accuracy and efficiency. On the other hand, generation of the contours with LiDAR data is more efficient and economical in terms of the quality and accuracy. In this study, the effects of various conditions of the pre-processing phase and the subsequent building extraction and contour generation phases for digital mapping have on the accuracy were investigated.