• Korean Journal of Pharmacognosy
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• v.17 no.3
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• pp.206-214
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• 1986

These studies were conducted an attempt to investigate effects of 'Paeryungtang' and 'Kamipaeryungtang' water extracts on diuretic, antipyretic, antiinflammatory and analgesic actions. The results of these studies were summarized as follows; Increase in urinary volume, urinary $Na^+$ excretions were significantly recognized in normal rat. Increase in urinary volume, urinary $Na^+\;and\;Cl^-$ excretions were significantly shown in rat with 2mg/kg $HgCl_2$-induced acute renal failure. Antipyretic, anti-inflammatory and analgesic effects of 'Paeryungtang' and 'Kamipaeryungtang' were recognized in mice, rats and rabbits.

• Korean Journal of Pharmacognosy
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• v.13 no.4
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• pp.157-162
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• 1982

This study was undertaken to investigate effects of 'Oyo-Tang' on the respiratory system. The results of this study were summarized as follows; 1. Antihistamine and relaxed dialating actions were recognized on the extracted ileum and serial broncheal samples in guinea-pigs. 2. Mild hypertensive action was recognized on the carotid artery in rabbits. 3. Antitussive action was noted on mechanically irritated coughs in dogs and cats. In connection with the results of these studies, effects based on the Oriental medical references were consistent with the actual experimental results. It was considered that effective application of 'Oyo-Tang' for the treatment of cough and asthma could be justified.

• The Korean Journal of Pharmacology
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• v.5 no.1
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• pp.23-28
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• 1969

Saponin, essential oil and fat oil fractions were fractionated from Panax Ginseng and their potentiating or inhibiting actions during the combined use of several central nervous system stimulants or depressants were observed to elucidate the possible role of Ginseng fractions on the central nervous system. Saponin, essential oil and fat oil fractions shortened nembutal sleeping time at low dosage (10 mg/kg) but contrarily they produced potentiation of nembutal hypnosis at high dosage (50mg/kg). In the toxicity study of amphetamine, saponin and essential oil fractions reduced the toxicity in aggregated mice at high dosage (100 mg/kg) but such decreased lethality was not observed in isolated mice. Ginseng fractions, especially high dose of saponin fraction (100mg/kg) prolonged the survival time after injection of convulsive dose of metrazol or cocaine and saponin fraction also prolonged the onset of cocaine convulsion at high dosage (100 mg/kg).

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2930-2932
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• 2000

In this paper. a robust gesture recognition system is designed and implemented to explore the communication methods between human and computer. Hand gestures in the proposed approach are used to communicate with a computer for actions of a high degree of freedom. The user does not need to wear any cumbersome devices like cyber-gloves. No assumption is made on whether the user is wearing any ornaments and whether the user is using the left or right hand gestures. Image segmentation based upon the skin-color and a shape analysis based upon the invariant moments are combined. The features are extracted and used for input vectors to a radial basis function networks(RBFN). Our "Puppy" robot is employed as a testbed. Preliminary results on a set of gestures show recognition rates of about 87% on the a real-time implementation.

• Structural Engineering and Mechanics
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• v.66 no.1
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• pp.75-84
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• 2018

This study presents the reliability-based analysis of nonlinear structures using the analytical fragility curves excited by random earthquake loads. The stochastic method of ground motion simulation is combined with the random vibration theory to compute structural failure probability. The formulation of structural failure probability using random vibration theory, based on only the frequency information of the excitation, provides an important basis for structural analysis in places where there is a lack of sufficient recorded ground motions. The importance of frequency content of ground motions on probability of structural failure is studied for different levels of the nonlinear behavior of structures. The set of simulated ground motion for this study is based on the results of probabilistic seismic hazard analysis. It is demonstrated that the scenario events identified by the seismic risk differ from those obtained by the disaggregation of seismic hazard. The validity of the presented procedure is evaluated by Monte-Carlo simulation.

• Journal of Korean Association for Spatial Structures
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• v.3 no.1 s.7
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• pp.77-85
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• 2003

This paper presents a truss model that can predict the shear behavior of reinforced concrete (RC) beams subjected to the combined actions of shear and flexure. Unlike other truss models, the proposed truss model, TATM, takes into account the effect of the flexural moment on the shear strength of RC beams with different shear span-to-depth ratios. To check the successfulness of the proposed model experimentally obtained stress shear strain curves were compared to the predicted ones using the proposed truss model. Furthermore, the shear strengths of 170 RC test beams with variable shear span-to-depth ratios were compared to the shear strengths as given by the truss model reported in this paper.

• Nuclear Engineering and Technology
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• v.36 no.6
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• pp.497-511
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• 2004

A PSV (pressurizer safety valve) popping test carried out in the early phases of a refueling outage may trigger a test-induced LOCA(loss of coolant accident) if a PSV fails to fully close and is stuck in a partially open position. According to a KSNP (Korea standard nuclear power plant) low power and shutdown PSA (probabilistic safety assessment), the failure of a high pressure safety injection (HPSI) accompanied by the failure of a PSV to fully close was identified as a dominant accident sequence with a significant impact on low power and shutdown risks (LPSR). In this study, we aim to investigate and verify a new means for mitigating this type of accident using a thermal-hydraulic analysis. In particular, we explore the applicability of an aggressive cool-down combined with operator actions. The results of the various sensitivity studies performed there will help reduce LPSR and improve Refueling outage safety.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.832-854
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• 2019

Action recognition has been studied in computer vision field for years. We present an effective approach to recognize actions using a dimension reduction method, which is applied as a crucial step to reduce the dimensionality of feature descriptors after extracting features. We propose to use sparse matrix and randomized kd-tree to modify it and then propose modified Local Fisher Discriminant Analysis (mLFDA) method which greatly reduces the required memory and accelerate the standard Local Fisher Discriminant Analysis. For feature encoding, we propose a useful encoding method called mix encoding which combines Fisher vector encoding and locality-constrained linear coding to get the final video representations. In order to add more meaningful features to the process of action recognition, the convolutional neural network is utilized and combined with mix encoding to produce the deep network feature. Experimental results show that our algorithm is a competitive method on KTH dataset, HMDB51 dataset and UCF101 dataset when combining all these methods.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.31-40
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• 2022

Coronary heart disease (CHD) is a comorbidity of COVID-19; therefore, routine early diagnosis is crucial. A large number of examination attributes in the context of diagnosing CHD is a distinct obstacle during the pandemic when the number of health service users is significant. The development of a precise machine learning model for diagnosis with a minimum number of examination attributes can allow examinations and healthcare actions to be undertaken quickly. This study proposes a CHD diagnosis model based on feature selection, data balancing, and ensemble-based classification methods. In the feature selection stage, a hybrid SVM-GA combined with fast correlation-based filter (FCBF) is used. The proposed system achieved an accuracy of 94.60% and area under the curve (AUC) of 97.5% when tested on the z-Alizadeh Sani dataset and used only 8 of 54 inspection attributes. In terms of performance, the proposed model can be placed in the very good category.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.483-503
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• 2014

In this paper, human action recognition using pyramid histograms of oriented gradients and collaborative multi-task learning is proposed. First, we accumulate global activities and construct motion history image (MHI) for both RGB and depth channels respectively to encode the dynamics of one action in different modalities, and then different action descriptors are extracted from depth and RGB MHI to represent global textual and structural characteristics of these actions. Specially, average value in hierarchical block, GIST and pyramid histograms of oriented gradients descriptors are employed to represent human motion. To demonstrate the superiority of the proposed method, we evaluate them by KNN, SVM with linear and RBF kernels, SRC and CRC models on DHA dataset, the well-known dataset for human action recognition. Large scale experimental results show our descriptors are robust, stable and efficient, and outperform the state-of-the-art methods. In addition, we investigate the performance of our descriptors further by combining these descriptors on DHA dataset, and observe that the performances of combined descriptors are much better than just using only sole descriptor. With multimodal features, we also propose a collaborative multi-task learning method for model learning and inference based on transfer learning theory. The main contributions lie in four aspects: 1) the proposed encoding the scheme can filter the stationary part of human body and reduce noise interference; 2) different kind of features and models are assessed, and the neighbor gradients information and pyramid layers are very helpful for representing these actions; 3) The proposed model can fuse the features from different modalities regardless of the sensor types, the ranges of the value, and the dimensions of different features; 4) The latent common knowledge among different modalities can be discovered by transfer learning to boost the performance.