• Journal of Korean Medicine Rehabilitation
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• v.21 no.2
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• pp.101-123
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• 2011

Objectives : The aim was to study the effect of bee venom pharmacopuncture therapy with different concentration on rheumatoid arthritis rat model. Methods : We enforced a bee venom pharmacopuncture therapy with different concentration on rheumatoid arthritis rat model by the intradermal injection of chicken type II collagen emulsified. 14 days after the onset of the rheumatoid arthritis rat model, a fixed volume of bee venom was daily injected to ST-35 acupoint in the rat's knee joint for 2-3 weeks. The hind paw volume, arthritic index, arthritic flexion pain test, pain threshold, and serum analysis (CRP, $PGE_2$, ALT, AST) were analyzed, and the expression profiles of COX-2, c-fos, and substance-P at the dorsal horn region of the spinal cord and subchondral bone of the knee joint were also analyzed by using the immunohistochemistry. Results : After the treatment of rheumatoid arthritis rats with bee venom pharmacopuncture, the paw volume of edema of arthritic rats were almost restored to the level of normal group, and behavior tests were very effective. Also the evaluation on the blood serum analysis was remarkable. COX-2, c-fos, and substance-P positive cells in the immunohistological section of dorsal horn region of the spinal cord and subchondral bone of the knee joints were significantly decreased. also the bee venom pharmacopuncture was effective to alleviate their rheumatoid arthritic inflammation cytokine inhibition as regards to the behavior tests and joint histological appearance. Conclusions : Based on the results in this study, bee venom pharmacopuncture with concentrated treatment condition was very effective in low fixed quantity and progressive low increased quantity.

• Speech Sciences
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• v.5 no.1
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• pp.7-21
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• 1999

This paper proposes an improved training procedure in speech recognition based on the continuous density of the Hidden Markov Model (CDHMM). Of the three parameters (initial state distribution probability, state transition probability, output probability density function (p.d.f.) of state) governing the CDHMM model, we focus on the third parameter and propose an efficient algorithm that determines the p.d.f. of each state. It is known that the resulting CDHMM model converges to a local maximum point of parameter estimation via the iterative Expectation Maximization procedure. Specifically, we propose two independent algorithms that can be embedded in the segmental K -means training procedure by replacing relevant key steps; the adaptation of the number of mixture Gaussian p.d.f. and the initialization using the CDHMM parameters previously estimated. The proposed adaptation algorithm searches for the optimal number of mixture Gaussian humps to ensure that the p.d.f. is consistently re-estimated, enabling the model to converge toward the global maximum point. By applying an appropriate threshold value, which measures the amount of collective changes of weighted variances, the optimized number of mixture Gaussian branch is determined. The initialization algorithm essentially exploits the CDHMM parameters previously estimated and uses them as the basis for the current initial segmentation subroutine. It captures the trend of previous training history whereas the uniform segmentation decimates it. The recognition performance of the proposed adaptation procedures along with the suggested initialization is verified to be always better than that of existing training procedure using fixed number of mixture Gaussian p.d.f.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.80.1-80.1
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• 2016

There are probabilistic forecast models for solar flare occurrence, which can be evaluated by various skill scores (e.g. accuracy, critical success index, heidek skill score, and true skill score). Since these skill scores assume that two types of forecast errors (i.e. false alarm and miss) are equal or constant, which does not take into account different situations of users, they may be unrealistic. In this study, we make an evaluation of a probabilistic flare forecast model [Lee et al., 2012] which use sunspot groups and its area changes as a proxy of flux emergence. We calculate daily solar flare probabilities from 2011 to 2014 using this model. The skill scores are computed through contingency tables as a function of forecast probability, which corresponds to the maximum skill score depending on flare class and type of a skill score. We use a value score with cost/loss ratio, relative importance between the two types of forecast errors. The forecast probability (y) is linearly changed with the cost/loss ratio (x) in the form of y=ax+b: a=0.88; b=0 (C), a=1.2; b=-0.05(M), a=1.29; b=-0.02(X). We find that the forecast model has an effective range of cost/loss ratio for each class flare: 0.536-0.853(C), 0.147-0.334(M), and 0.023-0.072(X). We expect that this study would provide a guideline to determine the probability threshold and the cost/loss ratio for space weather forecast.

• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.83-97
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• 2002

An experimental model which considers post-peak behaviors and pre-peak damage characteristics representing changes of elastic moduli in each damage level was developed. From experiments, some damage thresholds of rocks were determined, and regression analyses were carried out in order to represent changes of elastic moduli in each damage level as functions of confining pressure. In addition, it was intended to simulate post-peak behaviors with Hoek-Brown constants, $m_r\;and\;s_r$ for post-failure. The developed experimental model was implemented into $FLAC^{2D}$ by a FISH function. From results of parametric studies on Hoek-Brown constants for post-peak, it was revealed that uniaxial compressive strength more highly depends upon $s_r$, although it depends on both $m_r\;and\;s_r$. It was also shown that the post-peak slopes of stress-stain curves depend mainly on $m_r$. When the optimum models obtained from parametric studies were applied to numerical analysis, they predicted maximum strengths obtained from experiments and well simulated stiffness changes due to damage levels.

• Biomolecules & Therapeutics
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• v.25 no.3
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• pp.259-265
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• 2017

This study aimed to investigate the analgesic effect of substance P (SP) in an animal model of neuropathic pain. An experimental model of neuropathic pain, the chronic constriction injury (CCI) model, was established using ICR mice. An intravenous (i.v.) injection of SP (1 nmole/kg) was administered to the mice to examine the analgesic effects of systemic SP on neuropathic pain. Behavioral testing and immunostaining was performed following treatment of the CCI model with SP. SP attenuated mechanical allodynia in a time-dependent manner, beginning at 1 h following administration, peaking at 1 day post-injection, and decaying by 3 days post-injection. The second injection of SP also increased the threshold of mechanical allodynia, with the effects peaking on day 1 and decaying by day 3. A reduction in phospho-ERK and glial fibrillary acidic protein (GFAP) accompanied the attenuation of mechanical allodynia. We have shown for the first time that i.v. administration of substance P attenuated mechanical allodynia in the maintenance phase of neuropathic pain using von Frey's test, and simultaneously reduced levels of phospho-ERK and GFAP, which are representative biochemical markers of neuropathic pain. Importantly, glial cells in the dorsal horn of the spinal cord (L4-L5) of SP-treated CCI mice, expressed the anti-inflammatory cytokine, IL-10, which was not seen in vehicle saline-treated mice. Thus, i.v. administration of substance P may be beneficial for improving the treatment of patients with neuropathic pain, since it decreases the activity of nociceptive factors and increases the expression of anti-nociceptive factors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3B
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• pp.311-317
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• 2009

The traditional network anomaly detection systems execute the threshold-based detection without considering dynamic network environments, which causes false positive and limits an effective resource utilization. To overcome the drawbacks, we present the adaptive network anomaly detection model based on artificial immune system (AIS) in centralized network. AIS is inspired from human immune system that has learning, adaptation and memory. In our proposed model, the interaction between dendritic cell and T-cell of human immune system is adopted. We design the main components, such as central node and router node, and define functions of them. The central node analyzes the anomaly information received from the related router nodes, decides response policy and sends the policy to corresponding nodes. The router node consists of detector module and responder module. The detector module perceives the anomaly depending on learning data and the responder module settles the anomaly according to the policy received from central node. Finally we evaluate the possibility of the proposed detection model through simulation.

• Health Policy and Management
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• v.29 no.3
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• pp.357-367
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• 2019

Background: This study evaluated the cost-effectiveness of 21 different national dyslipidemia screening strategies according to total cholesterol (TC) cutoff and screening interval among 40 years or more for the primary prevention of coronary heart disease over a lifetime in Korea, from a societal perspective. Methods: A decision tree was used to estimate disease detection with the 21 different screening strategies, while a Markov model was used to model disease progression until death, quality-adjusted life years (QALYs) and costs from a Korea societal perspective. Results: The results showed that the strategy with TC 200 mg/dL and 4-year interval cost \4,625,446 for 16.65105 QALYs per person and strategy with TC 200 mg/dL and 3-year interval cost \4,691,771 for 16.65164 QALYs compared with \3,061,371 for 16.59877 QALYs for strategy with no screening. The incremental cost-effectiveness ratio of strategy with TC 200 mg/dL and 4-year interval versus strategy with no screening was \29,916,271/QALY. At a Korea willingness-to-pay threshold of \30,500,000/QALY, strategy with TC 200 mg/dL and 4-year interval is cost-effective compared with strategy with no screening. Sensitivity analyses showed that results were robust to reasonable variations in model parameters. Conclusion: In this study, revised national dyslipidemia screening strategy with TC 200 mg/dL and 4-year interval could be a cost-effective option. A better understanding of the Korean dyslipidemia population may be necessary to aid in future efforts to improve dyslipidemia diagnosis and management.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.3
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• pp.157-163
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• 2011

In this study, after the target basin was divided into both overland and channel grids, the travel time from center of each grid cell to watershed's outlet was calculated based on the manning equation. Through this process, volumetric discharge was calculated according to the isochrones and finally, the direct runoff hydrograph was estimated considering watershed's hydrodynamic characteristics. Sanseong subwatershed located in main stream of Bocheong basin was selected as a target basin. The model parameters are only two: area threshold and channel velocity correction factor; the optimized values were estimated at 3,800 and 3.3, respectively. The developed model based on the tuned parameters led to well-matching results between observed and calculated hydrographs (mean of absolute error of peak discharge: 3.41%, mean of absolute error of peak time: 0.67 hr). Moreover, the analysis results regarding histogram of travel time-contribution area demonstrates that the proposed model characterizes relatively well hydrodynamic characteristics of the catchment due to effective rainfall.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.127-134
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• 2021

In this study, an anomaly detection (AD) algorithm was implemented to detect the failure of a marine air compressor. A lab-scale experiment was designed to produce fault datasets (time-series electric current measurements) for 10 failure modes of the air compressor. The results demonstrated that the temporal pattern of the datasets showed periodicity with a different period, depending on the failure mode. An AD model with a convolutional autoencoder was developed and trained based on a normal operation dataset. The reconstruction error was used as the threshold for AD. The reconstruction error was noted to be dependent on the AD model and hyperparameter tuning. The AD model was applied to the synthetic dataset, which comprised both normal and abnormal conditions of the air compressor for validation. The AD model exhibited good detection performance on anomalies showing periodicity but poor performance on anomalies resulting from subtle load changes in the motor.

• The Journal of the Acoustical Society of Korea
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• v.27 no.7
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• pp.372-378
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• 2008

Voice activity detectors (VAD) are important in wireless communication and speech signal processing, In the conventional VAD methods, an expression for the likelihood ratio test (LRT) based on statistical models is derived in discrete Fourier transform (DFT) domain, Then, speech or noise is decided by comparing the value of the expression with a threshold, This paper presents a new statistical VAD method based on a signal subspace approach, The probabilistic principal component analysis (PPCA) is employed to obtain a signal subspace model that incorporates probabilistic model of noisy signal to the signal subspace method, The proposed approach provides a novel decision rule based on LRT in the signal subspace domain, Experimental results show that the proposed signal subspace model based VAD method outperforms those based on the widely used Gaussian distribution in DFT domain.