• Weed & Turfgrass Science
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• v.4 no.4
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• pp.279-287
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• 2015

The present research was carried out in order to investigate the occurrence of problematic weed species in Cnidium officinale Makino. Field in South Korea. Total 53 sites of the 3 different regions in S. Korea were investigated from May to October, 2014. In Cnidium officinale fields, the identified weeds were distributed in 35 families and 99 species. Total 5 communities that consist of Commelina communis, Eleocharis kuroguwai, Persicaria vulgaris, Chenopodium album-Acalypha australis, and Galinsoga ciliata dominated the appearance. The weeds occurred in Cnidium officinale fields were divided into three groups in principal component plot analysis (PCA). It was observed that in control weeds plots; 20 plants of Cnidium officinale fresh weight is 739.9 g while the uncontrolled plots have no Cnidium officinale plants. The current investigation could be useful for estimation of future weeds occurrence, weed flora dynamics and establishment of weed control methods in Cnidium officinale fields in Korea.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1281-1281
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• 2001

Near-infrared (NIR) spectra have bean measured for high-density (HDPE), linear low-density (LLDPE), and low-density (LDPE) polyethylene in pellet or thin films. The obtained spectra have been analyzed by conventional spectroscopic analysis methods and chemometrics. By using the second derivative, principal component analysis (PCA), and two-dimensional (2D) correlation analysis, we could separate many overlapped bands in the NIR. It was found that the intensities of some bands are sensitive to density and crystallinity of PE. This may be the first time that such bands in the NIR region have ever been discussed. Correlations of such marker bands among the NIR spectra have also been investigated. This sort of investigation is very important not only for further understanding of vibration spectra of various of PE but also for quality control of PE by vibrational spectroscopy. Figure 1 (a) and (b) shows a NIR reflectance spectrum of one of the LLDPE samples and that of PE, respectively. Figure 2 shows a PC weight loadings plot of factor 1 for a score plot of PCA for the 16 kinds of LLDPE and PE based upon their 51 NIR spectra in the 1100-1900 nm region. The PC loadings plot separates the bands due to the $CH_3$ groups and those arising form the $CH_2$ groups, allowing one to make band assignments. The 2D correlation analysis is also powerful in band enhancement, and the band assignments based upon PCA are in good agreement with those by the 2D correlation analysis.(Figure omitted). We have made a calibration model, which predicts the density of LLDPE by use of partial least square (PLS) regression. From the loadings plot of regression coefficients for the model , we suggest that the band at 1542, 1728, and 1764 nm very sensitive to the changes in density and crystalinity.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.5
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• pp.105-114
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• 2000

In this paper, we concentrate on an efficient method to decrease a lot of pixel data to be processed with an Image transform based automatic lipreading It is reported that the image transform based approach, which obtains a compressed representation of the speaker's mouth, results in superior lipreading performance than the lip contour based approach But this approach produces so many feature parameters of the lip that has much data and requires much computation time for recognition To reduce the data to be computed, we propose a simple method folding at the vertical center of the lip-image based on the symmetry of the lip In addition, the principal component analysis(PCA) is used for fast algorithm and HMM word recognition results are reported The proposed method reduces the number of the feature parameters at $22{\sim}47%$ and improves hidden Markov model(HMM)word recognition rates at $2{\sim}3%$, using the folded lip-image compared with the normal method using $16{\times}16$ lip-image.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.7143-7147
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• 2015

Gold nanoparticles (GNPs) were conjugated with gallic acid (GA) at various concentrations between 30 and $150{\mu}M$ and characterized using transmission electron microscopy (TEM) and UV-Vis spectroscopy (UV-VIS). The anticancer activities of the gallic acid-stabilized gold nanoparticles against well-differentiated (M213) and moderately differentiated (M214) adenocarcinomas were then determined using a neutral red assay. The GA mechanism of action was evaluated using Fourier transform infrared (FTIR) microspectroscopy. Distinctive features of the FTIR spectra between the control and GA-treated cells were confirmed by principal component analysis (PCA). The surface plasmon resonance spectra of the GNPs had a maximum absorption at 520 nm, whereas GNPs-GA shifted the maximum absorption values. In an in vitro study, the complexed GNPs-GA had an increased ability to inhibit the proliferation of cancer cells that was statistically significant (P<0.0001) in both M213 and M214 cells compared to GA alone, indicating that the anticancer activity of GA can be improved by conjugation with GNPs. Moreover, PCA revealed that exposure of the tested cells to GA resulted in significant changes in their cell membrane lipids and fatty acids, which may enhance the efficacy of this anticancer activity regarding apoptosis pathways.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3112-3112
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• 2001

Near-Infrared Spectroscopy (NIRS) was investigated aiming at the identification of falsified drugs. The identification is based on comparison of the NIR spectrum of a sample with a typical spectra of an authentic drug using multivariate modelling and classification algorithms (PCA/SIMCA). Two spectrophotometers (Brimrose - Luminar 2000 and 2030), based on acoustic-optical filter (AOTF) technology, sharing the same controlling computer, software (Brimrose - Snap 2.03) and the data acquisition electronics, were employed. The Luminar 2000 scans the range 850 1800 nm and was employed for transmitance/absorbance measurements of liquids with a transflectance optical bundle probe with total optical path of 5 mm and a circular area of 0.5 $\textrm{cm}^2$. Model 2030 scans the rage 1100 2400 nm and was employed for reflectance measurement of solids drugs. 300 spectra, acquired in about 20 s, were averaged for each sample. Chemometric treatment of the spectral data, modelling and classification were performed by using the Unscrambler 7.5 software (CAMO Norway). This package provides the Principal Component Analysis (PCA) and SIMCA algorithms, used for modelling and classification, respectively. Initially, NIRS was evaluated for spectrum acquisition of various drugs, selected in order to accomplish the diversity of physico-chemical characteristics found among commercial products. Parameters which could affect the spectra of a given drug (especially if presented as solid tablets) were investigated and the results showed that the first derivative can minimize spectral changes associated with tablet geometry, physical differences in their faces and position in relation to the probe beam. The effect of ambient humidity and temperature were also investigated. The first factor needs to be controlled for model construction because the ambient humidity can cause spectral alterations that should cause the wrong classification of a real drug if the factor is not considered by the model.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1692-1700
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• 2009

This paper proposes the direction distribution of surface normal vectors as a feature descriptor of three-dimensional models. Proposed the feature descriptor handles rotation invariance using a principal component analysis(PCA) method, and performs mesh simplification to make it robust and nonsensitive against noise addition. Our method picks samples for the distribution of normal vectors to be proportional to the area of each polygon, applies weight to the normal vectors, and applies interpolation to enhance discrimination so that the information on the surface with less area may be less reflected on composing a feature descriptor. This research measures similarity between models with a L1-norm in the probability density histogram where the distances of feature descriptors are normalized. Experimental results have shown that the proposed method has improved the retrieval performance described in an average normalized modified retrieval rank(ANMRR) by about 17.2% and the retrieval performance described in a quantitative discrimination scale by 9.6%~17.5% as compared to the existing method.

• Journal of Microbiology and Biotechnology
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• v.22 no.9
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• pp.1264-1270
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• 2012

The ecotoxicological effects of nanomaterials on animal, plant, and soil microorganisms have been widely investigated; however, the nanotoxic effects of plant-soil interactive systems are still largely unknown. In the present study, the effects of ZnO nanoparticles (NPs) on the soil-plant interactive system were estimated. The growth of plant seedlings in the presence of different concentrations of ZnO NPs within microcosm soil (M) and natural soil (NS) was compared. Changes in dehydrogenase activity (DHA) and soil bacterial community diversity were estimated based on the microcosm with plants (M+P) and microcosm without plants (M-P) in different concentrations of ZnO NPs treatment. The shoot growth of M+P and NS+P was significantly inhibited by 24% and 31.5% relative to the control at a ZnO NPs concentration of 1,000 mg/kg. The DHA levels decreased following increased ZnO NPs concentration. Specifically, these levels were significantly reduced from 100 mg/kg in M-P and only 1,000 mg/kg in M+P. Different clustering groups of M+P and M-P were observed in the principal component analysis (PCA). Therefore, the M-P's soil bacterial population may have more toxic effects at a high dose of ZnO NPs than M+P's. The plant and activation of soil bacteria in the M+P may have a less toxic interactive effect on each of the soil bacterial populations and plant growth by the ZnO NPs attachment or absorption of plant roots surface. The soil-plant interactive system might help decrease the toxic effects of ZnO NPs on the rhizobacteria population.

• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.37-45
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• 2013

Context dependent fusion (CDF) is a fusion algorithm that combines multiple outputs from different classifiers to achieve better performance. CDF tries to divide the problem context into several homogeneous sub-contexts and to fuse data locally with respect to each sub-context. CDF showed better performance than existing methods, however, it is sensitive to noise due to the large number of parameters optimized and the innate linearity limits the application of CDF. In this paper, a variant of CDF using support vector machines (SVMs) for fusion and kernel principal component analysis (K-PCA) for context extraction is proposed to solve the problems in CDF, named CDF-SVM. Kernel PCA can shape irregular clusters including elliptical ones through the non-linear kernel transformation and SVM can draw a non-linear decision boundary. Regularization terms is also included in the objective function of CDF-SVM to mitigate the noise sensitivity in CDF. CDF-SVM showed better performance than CDF and its variants, which is demonstrated through the experiments with a landmine data set.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.962-969
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• 2006

To address the importance of the process fault detection for productivity, support vector machines (SVMs) is employed to assist the decision to determine process faults in real-time. The reactive ion etching (RIE) tool data acquired from a production line consist of 59 variables, and each of them consists of 10 data points per second. Principal component analysis (PCA) is first performed to accommodate for real-time data processing by reducing the dimensionality or the data. SVMs for eleven steps or etching m are established with data acquired from baseline runs, and they are further verified with the data from controlled (acceptable) and perturbed (unacceptable) runs. Then, each SVM is further utilized for the fault detection purpose utilizing control limits which is well understood in statistical process control chart. Utilizing SVMs, fault detection of reactive ion etching process is demonstrated with zero false alarm rate of the controlled runs on a run to run basis.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.3
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• pp.315-327
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• 2017

In this paper, we present a method for estimating of position and velocity of a moving target by using the range and the bearing measurements from multiple sensors of aiming unit. In many cases, conventional low cost gyro sensor and a portable laser range finder(LRF) degrade the accuracy of estimation. To enhance these problems, we propose two methods. The first is background image tracking and the other is principal component analysis (PCA). The background tracking is used to assist the low cost gyro censor. And the PCA is used to cope with the problems of a portable LRF. In this paper, we prove that our method is robust with respect to low-frequency, biased and noisy inputs. We also present a comparison between our method and the extended Kalman filter(EKF).