• Journal of Ginseng Research
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• v.33 no.2
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• pp.93-98
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• 2009

To understand the anti-allergic mechanism of compound K, which is a metabolite of ginsenoside Rb1, a main constituent of the root of Panax ginseng C.A. Meyer (family Araliaceae), its inhibitory effect against IgE-antigen complex IAC)-induced passive cutaneous anaphylaxis (PCA) reaction in mice and mRNA and protein expressions of allergic cytokines in lAC-stimulated RBL-2H3 cells were investigated. Orally administered ginsenoside Rb1 more potently inhibited PCA reaction when administered at 5 h prior to the lAC treatment than when administered at I h before. However, compound K orally administered 1 h before lAC treatment showed a more potent anti-PCA reaction effect than when treated at 5 h before. Orally administered ginsenoside Rb1 more potently inhibited PCA reaction induced by lAC in mice than intraperitoneally treated one, apart from orally administered its metabolite, compound K, which was more potent than the orally administered one. The compound K, a metabolite of ginsenoside Rb1, inhibited mRNA and protein expressions of IL-4 and TNF-${\alpha}$ and the activation of their transcription factor NF-$\kappa$B and MAPK in lAC-stimulated RBL-2H3 cells. These findings suggest that orally administered ginsenoside Rb1 may be dependent on its metabolism by intestinal microflora in the intestine and the compound K may improve allergic diseases by the inhibition of IL-4 and TNF-${\alpha}$ expresseion.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.384.1-384.1
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• 2016

We reported that amorphous silicon (a-Si) thin film provide sample plate exhibiting a multimodality to measure biomolecules by secondary ion mass spectrometry (SIMS) and laser desorption/ionization mass spectrometry (LDI-MS). Kim et al.1 reported that a-Si thin film were suitable to detect small molecules such as drugs and peptides by SIMS and LDI-MS. Recently, bacterial identification has been required in many fields such as food analysis, veterinary science, ecology, agriculture, and so on.2 Mass spectrometry is emerging for identifying and profiling microbiology samples from its advantageous characters of label-free and shot-time analysis. Five species of bacteria - S. aureus, G. glutamicum, B. kurstaki, B. sphaericus, and B. licheniformis - were sampled for MS analysis without lipid extraction in sample preparation steps. The samples were loaded onto the a-Si thin film with a thickness of 100 nm which did not only considered laser-beam penetration but also surface homogeneity. Mass spectra were recorded in both positive and negative ionization modes for more analytical information. High reproducibility and sensitivity of mass spectra were demonstrated in a mass range up to mass-to-charge ratio(m/z) 1200 by applying the a-Si thin film in mentioned above MS. Principle component analysis (PCA) - a popular statistical analysis widely used in data processing was employed to differentiate between five bacterial species. The PCA results verified that each bacterial species were readily distinguished and differentiated effectively from our MS approach. It shows a new opportunity to rapid bacterial profiling and identification in clinical microbiology. More details will be discussed in the presentation.

• The Korean Journal of Pain
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• v.29 no.2
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• pp.110-118
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• 2016

Background: Nefopam is a non-opioid, non-steroidal, centrally acting analgesic drug. The concomitant use of opioids and nefopam is believed to have many advantages over the administration of opioids alone for postoperative pain management. We conducted a randomized, double-blind study to determine the fentanyl-sparing effect of co-administration of nefopam with fentanyl for postoperative pain management via patient controlled analgesia (PCA). Methods: Ninety female patients who underwent laparoscopic total hysterectomy under general anesthesia were randomized into 3 groups, Group A, fentanyl $1,000{\mu}g$; Group B, fentanyl $500{\mu}g$ + nefopam 200 mg; and Group C, fentanyl $500{\mu}g$ + nefopam 400 mg, in a total volume of 100 ml PCA to be administered over the first 48 h postoperatively without basal infusion. The primary outcome was total fentanyl consumption during 48 h; secondary outcomes included pain scores and incidence of side effects. Results: Eighty-one patients were included in the analysis. The overall fentanyl-sparing effects of PCA with concomitant administration of nefopam during the first 48 h postoperatively were 54.5% in Group B and 48.9% group C. Fentanyl use was not significantly different between Groups B and C despite the difference in the nefopam dose. There were no differences among the three groups in terms of PCA-related side effects, although the overall sedation score of Group B was significantly lower than that of Group A. Conclusions: The concomitant administration of nefopam with fentanyl for postoperative pain management may allow reduction of fentanyl dose, thereby reducing the risk of opioid-related adverse effects.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.7-14
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• 2006

This paper presents an efficient face recognition method using both first moment of image and basis images. First moment which is a method for finding centroid of image, is applied to exclude the needless backgrounds in the face recognitions by shifting to the centroid of face image. Basis images which are the face features, are respectively extracted by principal component analysis(PCA) and fixed-point independent component analysis(FP-ICA). This is to improve the recognition performance by excluding the redundancy considering to second- and higher-order statistics of face image. The proposed methods has been applied to the problem for recognizing the 48 face images(12 persons*4 scenes) of 64*64 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed methods has a superior recognition performances(speed, rate) than conventional PCA and FP-ICA without preprocessing, the proposed FP-ICA has also better performance than the proposed PCA. The city-block has been relatively achieved more an accurate similarity than Euclidean or negative angle.

• Korean Journal of Crystallography
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• v.2 no.2
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• pp.22-27
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• 1991

4-Amino-n-(2,6-dimethyk4-pyrimidnyl) benzenesulfonamide, C12H14N402. Unit cell parameters are a =12.626, b=11.262, c=9.375, a:b:r=90°, V =1333.07h3, D,at=1.390 g /cm3, and λ(Cu-Ka)=1.5418, The space group is Pca21, Orthorhombic. The final R factor of 1068 unique observed reflections is R=0.040. Two pair of molecules which related by symmetry operation has strong hydrogen bond. One is between H(N2) and N(3), and the other is H(NIA) and 0(1).

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3105-3105
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• 2001

Ingests and fecal contamination on a poultry carcass is a food safety hazard due to potential microbiological contamination. A visible/near-infrared (NIR) spectrometer was used to discriminate among pure ingesta and fecal material, breast skin contaminated with ingesta or fecal material and uncontaminated breast skin. Birds were fed isocaloric diets formulated with either maize, mile, or wheat and soybean meal for protein requirements. Following completion of the feeding period (14 days), the birds were humanely processed and eviscerated to obtain ingests from the crop or proventriculus and feces from the duodenum, ceca, and colon portion of the digestive tract. Pure feces and ingesta, breast skin, and contaminated breast skin were scanned from 400 to 2500 nm and analyzed from 400 to 900 nm. Principal component analysis (PCA) of reflectance spectra was used to discriminate between contaminates and uncontaminated breast skin. Results indicate that visible (400 to 760 nm) and NIR 760-900 nm spectra can detect contaminates. From PCA analysis, key wavelengths were identified for discrimination of uncontaminated skin from contaminates based the evaluation of loadings weights.

• Journal of Ocean Engineering and Technology
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• v.26 no.4
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• pp.57-63
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• 2012

In this paper, a Bayesian classifier based on PCA (principle component analysis) is proposed to classify underwater transient signals using $16^{th}$ order LPC (linear predictive coding) coefficients as feature vector. The proposed classifier is composed of two steps. The mechanical signals were separated from biological signals in the first step, and then each type of the mechanical signal was recognized in the second step. Three biological transient signals and two mechanical signals were used to conduct experiments. The classification ratios for the feature vectors of biological signals and mechanical signals were 94.75% and 97.23%, respectively, when all 16 order LPC vector were used. In order to determine the effect of underwater noise on the classification performance, underwater ambient noise was added to the test signals and the classification ratio according to SNR (signal-to-noise ratio) was compared by changing dimension of feature vector using PCA. The classification ratios of the biological and mechanical signals under ocean ambient noise at 10dB SNR, were 0.51% and 100% respectively. However, the ratios were changed to 53.07% and 83.14% when the dimension of feature vector was converted to three by applying PCA. For correct, classification, it is required SNR over 10 dB for three dimension feature vector and over 30dB SNR for seven dimension feature vector under ocean ambient noise environment.

• Journal of Drive and Control
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• v.11 no.4
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• pp.39-45
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• 2014

With recent oil price increases, the fuel efficiency of hydraulic excavators has become a serious issue. Researchers have considered weight lightening by high pressurization in order to improve the efficiency of the excavator and pump controlled actuation (PCA) and to reduce pressure loss of hybrid and valves using mechanical inertia. However, high pressurization is not very effective because the excavator operates at a low speed; a hybrid is inefficient because little accumulated inertial energy is accumulated; and PCA is ineffective because control precision and responsibility are low. In this study, a method to minimize air and gas in hydraulic oil has been presented as a simple and new way to increase hydraulic efficiency.

• The Journal of Fisheries Business Administration
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• v.14 no.2
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• pp.1-14
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• 1983

This paper considers a classification of the 88 islands located at Kyong-nam area in Korea, using by examples of 12 components of the islands. By means of principal component analysis 2 principle components were extracted, which explained a total of 73.7% of the variance. Using an eigen variable criterion (λ>1), no further principle components were discussed. Principal component 1 and 2 explained 63.4% and 10.3% of the total variance respectively, The representation of the unrelated factor scores along the first and second principal axes produced a new information with respect to the classification of the islands. Based upon the representation, 88 islands were classified into 6 groups i. e. A, B, C, D, E, and F according to similarity of the components among them in this paper. The "Group F" belongs to a miscellaneous assortment that does not fit into the logical category. category.

• Journal of KIISE
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• v.44 no.3
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• pp.295-305
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• 2017

Facial appearance is greatly influenced by illumination conditions, and therefore illumination variation is one of the factors that degrades performance of face recognition systems. In this paper, we propose a robust method for face representation under varying illumination conditions, combining non-alpha Weberface (non-alpha WF) and histogram equalization. We propose a two-step method: (1) for a given face image, non-alpha WF, which is not applied a parameter for adjusting the intensity difference between neighboring pixels in WF, is computed; (2) histogram equalization is performed to non-alpha WF, to make a uniform histogram distribution globally and to enhance the contrast. $(2D)^2PCA$ is applied to extract low-dimensional discriminating features from the preprocessed face image. Experimental results on the extended Yale B face database and the CMU PIE face database show that the proposed method yielded better recognition rates than several illumination processing methods as well as the conventional WF, achieving average recognition rates of 93.31% and 97.25%, respectively.