• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.2
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• pp.26-35
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• 2006

Design parameters of manned and unmanned rotorcrafts have been investigated to construct a design database and to derive trend curves. Design parameters of 78 manned rotorcrafts and 33 unmanned rotorcrafts have been collected and analyzed using linear regression method. Six kinds of trend curves equations are derived. Most of trend curves derived are relatively meaningful according to the calculated correlation and determination coefficients. The comparisons between manned and unmanned rotorcraft characteristics are performed. It has been drawn according to the comparisons that unmanned rotorcraft has smaller main rotor diameter and maximum take-off weight, bigger tail rotor size and similar level of empty weight fraction than manned rotorcraft.

• Journal of Physiology & Pathology in Korean Medicine
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• v.34 no.4
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• pp.184-190
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• 2020

This study was performed to find antibacterial substances contained in Scutellariae Radix (SR) using a systems pharmacological analysis method and to establish an effective strategy for the prevention and treatment of infectious diseases. Analysis of the main active ingredients of SR was performed using Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database and Analysis Platform. 36 active compounds were screened by the parameter values of Drug-Likeness (DL), Oral Bioavailability (OB), and Caco-2 permeability (Caco-2), which were based on the drug absorption, distribution, metabolism, and excretion indicators. The UniProt database was used to obtain information on 159 genes associated with active compounds. The main active compounds with antibacterial effects were wogonin, β-sitosterol, baicalein, acacetin and oroxylin-A. Target proteins associated with the antibacterial action were chemokine ligand 2, interleukin-6, tumor necrosis factor, caspase-8,9 and mitogen-activated protein kinase 14. In the future, systems pharmacological analysis of traditional medicine will be able to make it easy to find the important mechanism of action of active substances present in natural medicines and to optimize the efficacy of medicinal effects for combinations of major ingredients to help treat certain diseases.

• Structural Engineering and Mechanics
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• v.86 no.4
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• pp.547-571
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• 2023

To deeply probe the actual earthquake level and fragility of typical reinforced concrete (RC) structures under multiple intensity grades, considering diachronic measurement building stock samples and actual observations of representative catastrophic earth shocks in China from 1990 to 2010, RC structures were divided into traditional RC structures (TRCs) and bottom reinforced concrete frame seismic wall masonry (BFM) structures, and the empirical damage characteristics and mechanisms were analysed. A great deal of statistics and induction were developed on the historical experience investigation data of 59 typical catastrophic earthquakes in 9 provinces of China. The database and fragility matrix prediction model were established with TRCs of 4,122.5284×104 m² and 5,844 buildings and BFMs of 5,872 buildings as empirical seismic damage samples. By employing the methods of structural damage probability and statistics, nonlinear prediction of seismic vulnerability, and numerical and applied functional analysis, the comparison matrix of actual fragility probability prediction of TRC and BFM in multiple intensity regions under the latest version of China's macrointensity standard was established. A novel nonlinear regression prediction model of seismic vulnerability was proposed, and prediction models considering the seismic damage ratio and transcendental probability parameters were constructed. The time-varying vulnerability comparative model of the sample database was developed according to the different periods of multiple earthquakes. The new calculation method of the average fragility prediction index (AFPI) matrix parameter model has been proposed to predict the seismic fragility of an areal RC structure.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.290-297
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• 2009

This paper presents an improved adaptive multi-rate wideband (AMR-WB) algorithm for the efficient Text-To-Speech (TTS) database compression. The proposed algorithm includes unnecessary common bit-stream (CBS) removal and parameter delta coding combined with speaker-dependent huffman coding to reduce the required bit-rate without any quality degradation. We also propose lossy coding schemes to produce the maximum bit-rate reduction with negligible quality degradation. The proposed lossless algorithm including CBS removal can reduce bit-rate by 12.40% without quality degradation compared with the 12.65 kbps AMR-WB mode. The proposed lossy algorithm can reduce bit-rate by 20.00% with 0.12 PESQ degradation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.10
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• pp.1139-1145
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• 2014

Creation of a human skeleton model and characterization of the variation in the bone shape are fundamentally important in many applications of biomechanics. In this paper, we present a parametric shape modeling method for femurs that is based on extracting the main parameter of variations of the femur shape from a 3D model database by using statistical shape analysis. For this shape analysis, principal component analysis (PCA) is used. Application of the PCA to 3D data requires bringing all the models in correspondence to each other. For this reason, anatomical landmarks are used for guiding the deformation of the template model to fit the 3D model data. After subsequent application of PCA to a set of femur models, we calculate the correlation between the dominant components of shape variability for a target population and the anatomical parameters of the femur shape. Finally, we provide tools for visualizing and creating the femur shape using the main parameter of femur shape variation.

• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1465-1473
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• 2006

In this paper, we present an approach for facial expression recognition using Active Shape Models(ASM) and a state-based model in image sequences. Given an image frame, we use ASM to obtain the shape parameter vector of the model while we locate facial feature points. Then, we can obtain the shape parameter vector set for all the frames of an image sequence. This vector set is converted into a state vector which is one of the three states by the state-based model. In the classification step, we use the k-NN with the proposed similarity measure that is motivated on the observation that the variation-regions of an expression sequence are different from those of other expression sequences. In the experiment with the public database KCFD, we demonstrate that the proposed measure slightly outperforms the binary measure in which the recognition performance of the k-NN with the proposed measure and the existing binary measure show 89.1% and 86.2% respectively when k is 1.

• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.129-135
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• 2004

In the home health management system, we often face the situation to handle biological signals that are frequently measured from normal subjects. In such a case, it is necessary to decide whether the signal at a certain moment is normal or abnormal. Since ECC is one of the most frequently measured biological signals, we describe algorithms that detect QRS-complex and decide whether it is normal or abnormal. The developed QRS detection algorithm is a simplified version of the conventional algorithm providing enough performance for the proposed application. The developed classification algorithm that detects abnormal from mostly normal beats is based on QRS width, R-R interval and QRS shape parameter using Karhunen-Loeve transformation. The simplified QRS detector correctly detected about 99% of all beats in the MTT/BIH ECG database. The classification algorithm correctly classified about 96% of beats as normal or abnormal. The QRS detection and classification algorithm described in this paper could be used in home health management system.

• Journal of Preventive Medicine and Public Health
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• v.37 no.3
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• pp.246-252
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• 2004

Objectives : Cancer is the leading cause of death in Korea. Therefore, a National Cancer Screening Program (NCSP) was launched in 1999. This study planned to evaluate the performance of the NCSP to identifying the influencing factors in relation to characteristic public health centers. Methods : To analyze the performance, the database of the NCSP records for 2002 was used. The performance index was measured by the goal achievement rate, which was defined by the real number of screenees against the expected number of screenees. Also, a survey was conducted by a self-administered questionnaire to identify the factors associated with the goal achievement rate. The questionnaire was divided into two sections. In the first section, the individual characteristics of the program coordinator in each public health center were measured, and second section was comprised of questions about the organizational characteristics associated with the NCSP. A total of 121 subjects from 241 public health centers completed the questionnaire. Results : Of the 121 public health centers (50.2% response rate), the average goal achievement rate was 72.8%. The results of the regression model showed that public health centers located in rural area (parameter estimates=38.2) and had great support from a head of center or province (parameter estimates=0.20) and tended to have higher goal achievement rates. However, the characteristics of the program coordinator, especially their knowledge of and attitude toward cancer screening, were not significantly related to the goal achievement rates. Conclusions : It appears that the most important associated factors to the goal achievement rate in the NSCP were the location of the public health center and the support for the NCSP from the head of the center or province.

• Animal Bioscience
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• v.34 no.6
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• pp.949-956
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• 2021

Objective: This study was focused on the estimation of parameters of Wood's model and description of the lactation curve using the cows which were lactated over 24 months on the first lactation. Methods: The database included 1,333 pure-bred dairy Simmental primiparous cows which lactated for 24 months (732 days). The initial dataset entering the procedure of assessment of parameters of Wood's function included 35,826 milk yield records. Milk yield was recorded throughout lactation, with the earliest record taken on day 6 and the latest on day 1,348 of lactation. This dataset was used for the assessment of parameters a, b, c of Wood's model using the non-linear statistical procedure. These parameters were estimated for different length of lactation. The assessed parameters were used for calculation of some characteristics of lactation curves. Results: The lowest value of a parameter (15.2317) of Wood's model of lactation curve was found out in lactations up to 305 days long, contrary to b and c parameters which were highest in those lactations (0.1029 and 0.0015, respectively). The maximum value of a parameter (17.4329) was found out in lactations up to 640 days long, unlike b and c parameters which were minimal in those lactations (0.0603 and 0.0010, respectively). Conclusion: It can be concluded that the parameters of Wood's model and the shape of lactation curve are changing with the growing number of milk yield records. Also, the assessed parameters revealed a significant milk production potential after 305 days of lactation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1341-1347
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• 2020

Legacy studies for classifying arrhythmia have been studied in order to improve the accuracy of classification, Neural Network, Fuzzy, Machine Learning, etc. In particular, deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose parameter extraction based on AR and arrhythmia classification through a deep learning. For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The classification rate of PVC is evaluated through MIT-BIH arrhythmia database. The achieved scores indicate arrhythmia classification rate of over 97%.