• The Korea Journal of Herbology
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• v.28 no.5
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• pp.95-101
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• 2013

Objectives : A quantitative method using high performance liquid chromatography with a photodiode array detector(HPLC-PDA) was established for the quantitative analysis of the four main compound and pattern analysis to classification Piiellia ternate, P. pedatisecta and Typhonium flagelliforme. Methods : The analytical procedure for the determination of P. ternata, together with the known main compounds uracil, uridine, guanosine and adenosine was established. Optimum HPLC-PDA separation of these P. ternata was possible on Luna C18(2) column material, using water and acetonitrile as mobile phase. The method was validated according to regulatory guidelines. In addition, this assay method were analyzed for the content of four main compound in P. ternata, P. pedatisecta and T. flagelliforme and by data obtained from the HPLC-PDA analysis was performed principal component analysis(PCA). Results : Validation results indicated that the HPLC method is well suited for the determination of the roots of P. ternata with a good linearity ($r^2$ > 0.999), precision and recovery rates. Analysis of HPLC-PDA, the average content of uracil, uridine, guanosine and adenosine was significantly higher in P. ternate>P. pedatisecta> T. flagelliforme order. The application of PCA to main compound data by HPLC-PDA permitted the effective discrimination among the three species. Conclusions : Analysis of both HPLC-PDA and PCA confirmed the fact that four main compound and pattern profiles of P. ternata, P. pedatisecta and T. flagelliforme were different from each other.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.6
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• pp.12-18
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• 2010

Automatic Target Detection (ATD) systems that use forward-looking infrared (FLIR) consists of three stages. preprocessing, detection, and clutter rejection. All potential targets are extracted in preprocessing and detection stages. But, this results in a high false alarm rates. To reduce false alarm rates of ATD system, true targets are extracted in the clutter rejection stage. This paper focuses on clutter rejection stage. This paper presents a new clutter rejection technique using PCA features and stochastic features of clutters and targets. PCA features are obtained from Euclidian distances using which potential targets are projected to reduced eigenspace selected from target eigenvectors. CV is used for calculating stochastic features of edges in targets and clutters images. To distinguish between target and clutter, LDA (Linear Discriminant Analysis) is applied. The experimental results show that the proposed algorithm accurately classify clutters with a low false rate compared to PCA method or CV method

• Journal of Microbiology and Biotechnology
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• v.34 no.4
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• pp.958-968
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• 2024

In recent years, there has been a growing recognition of the important role that long non-coding RNAs (lncRNAs) play in the immunological process of hepatocellular carcinoma (LIHC). An increasing number of studies have shown that certain lncRNAs hold great potential as viable options for diagnosis and treatment in clinical practice. The primary objective of our investigation was to devise an immune lncRNA profile to explore the significance of immune-associated lncRNAs in the accurate diagnosis and prognosis of LIHC. Gene expression profiles of LIHC samples obtained from TCGA database were screened for immune-related genes. The optimal immune-related lncRNA signature was built via correlational analysis, univariate and multivariate Cox analysis. Then, the Kaplan-Meier plot, ROC curve, clinical analysis, gene set enrichment analysis, and principal component analysis were performed to evaluate the capability of the immune lncRNA signature as a prognostic indicator. Six long non-coding RNAs were identified via correlation analysis and Cox regression analysis considering their interactions with immune genes. Subsequently, tumor samples were categorized into two distinct risk groups based on different clinical outcomes. Stratification analysis indicated that the prognostic ability of this signature acted as an independent factor. The Kaplan-Meier method was employed to conduct survival analysis, results showed a significant difference between the two risk groups. The predictive performance of this signature was validated by principal component analysis (PCA). Additionally, data obtained from gene set enrichment analysis (GSEA) revealed several potential biological processes in which these biomarkers may be involved. To summarize, this study demonstrated that this six-lncRNA signature could be identified as a potential factor that can independently predict the prognosis of LIHC patients.

• Journal of the Korean Society of Food Culture
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• v.22 no.4
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• pp.462-467
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• 2007

Volatile components of six commercial $sauces(A{\sim}F)$ and $\hat{O}yuk-jang$(G, H), a Korean traditional fermented sauce, were analyzed by electronic nose based on GC with surface acoustic wave(SAW) sensor. The obtained data were used for pattern recognition and a visual pattern called a $VaporPrint^{TM}$, derived from the frequency and chromatogram of the GC-SAW sensor. Volatile components of sauces and $\hat{O}yuk-jang$ were well discriminated with the direct use of $VaporPrint^{TM}$. Commercial sauces and $\hat{O}yuk-jang$ showed different volatile patterns, respectively, due to different major material, which meju, beef extract, pickled anchovies, and Katsuobushi were used. Volatile components of Oyuk-jang were decreased drastically during the fermentation time. After boiling $\hat{O}yuk-jang$, new several peaks were found. The responses by electronic nose were used for principal component analysis. The PCA plot showed that volatile components pattern were well discriminated by first principal component score(proportion: 96.8%), and first principal component score of $\hat{O}yuk-jang$ was between soy sauce of the liquid extracted from beef and sauce of pickled anchovies.

• The Journal of the Acoustical Society of Korea
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• v.19 no.2
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• pp.40-45
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• 2000

In speech corpus generation and speech recognition, it is sometimes needed to segment the input speech data without any prior knowledge. A method to accomplish this kind of segmentation, often called as blind segmentation, or acoustic segmentation, is to find boundaries which minimize the Euclidean distances among the feature vectors of each segments. However, the use of this metric alone is prone to errors because of the fluctuations or variations of the feature vectors within a segment. In this paper, we introduce the principal component analysis method to take the trend of feature vectors into consideration, so that the proposed distance measure be the distance between feature vectors and their projected points on the principal components. The proposed distance measure is applied in the LBDP(level building dynamic programming) algorithm for an experimentation of continuous speech segmentation. The result was rather promising, resulting in 3-6% reduction in deletion rate compared to the pure Euclidean measure.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.157-163
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• 2013

In this paper, we propose a human detection technique using thermal imaging camera. The proposed method is useful at night or rainy weather where a visible light imaging cameras is not able to detect human activities. Under the observation that a human is usually brighter than the background in the thermal images, we estimate the preliminary human regions using the statistical confidence measures in the gray-level, brightness histogram. Afterwards, we applied Gaussian filtering and blob labeling techniques to remove the unwanted noise, and gather the scattered of the pixel distributions and the center of gravities of the blobs. In the final step, we exploit the aspect ratio and the area on the unified object region as well as a number of the principal components extracted from the object region images to determine if the detected object is a human. The experimental results show that the proposed method is effective in environments where visible light cameras are not applicable.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.83-83
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• 2017

The National Agrobiodiversity Center (NAS, RDA, Republic of Korea) has continually collected new valuable genetic resources. In this study, we regenerated conserved kidney bean (Phaseolus vulgaris L.) germplasm which couldn't be available because of seed quantity and quality, and we also surveyed their morphological characters for the sustainable utilization. A total of 431 kidney bean accessions were regenerated and 18 morphological traits were surveyed according to the characterization guideline of RDA Genebank. Among the surveyed traits, flowering time ranged from May 23 to September 4 and 73.8% of tested accessions were mainly flowering in June. The maturity time ranged from July 1 to October 15 and main flowering time was July (91.4%). For plant type, 270 accs (62.6%) were climbing type followed by medium type of 86 accs (20.0%) and dwarf type of 65 accs (15.1%). The seed coat colors were various; yellow (34.6%), white (22.3%), brown (17.9%), red (10.7%), black (5.8%), violet (11%), pink (1.4%), navy (0.9%). Principal component analysis indicated that five principal components (PCs) with Eigen values >1 accounted for more than 65.8% variability. The first PC was more related to growth habits such as growth type, flowering time, and plant type. The second and third PCs showed higher values of the pigment characters such as seed coat color, flower color, and pod color. In fourth and fifty PCs, there were the higher positive values of the pod shapes. Our results provided insight into the characteristics kidney beans, thus the utilization basis of kidney beans might be elevated for bio-industry.

• Nuclear Engineering and Technology
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• v.48 no.5
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• pp.1184-1191
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• 2016

This paper introduces the development of a transient monitoring system to detect the early stage of a transient, to identify the type of the transient scenario, and to inform an operator with the remaining time to turbine trip when there is no operator's relevant control. This study focused on the transients originating from a secondary system in nuclear power plants (NPPs), because the secondary system was recognized to be a more dominant factor to make unplanned turbine-generator trips which can ultimately result in reactor trips. In order to make the proposed methodology practical forward, all the transient scenarios registered in a simulator of a 1,000 MWe pressurized water reactor were archived in the transient pattern database. The transient patterns show plant behavior until turbine-generator trip when there is no operator's intervention. Meanwhile, the operating data periodically captured from a plant computer is compared with an individual transient pattern in the database and a highly matched section among the transient patterns enables isolation of the type of transient and prediction of the expected remaining time to trip. The transient pattern database consists of hundreds of variables, so it is difficult to speedily compare patterns and to draw a conclusion in a timely manner. The transient pattern database and the operating data are, therefore, converted into a smaller dimension using the principal component analysis (PCA). This paper describes the process of constructing the transient pattern database, dealing with principal components, and optimizing similarity measures.

• Food Science of Animal Resources
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• v.34 no.5
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• pp.700-708
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• 2014

The volatile composition of veal has yet to be reported and is one of the important factors determining meat character and quality. To identify the most important aroma compounds in veal from Holstein bull calves fed one of three diets, samples were subjected to solid-phase microextraction (SPME) combined with gas chromatography-quadrupole mass spectrometry (GC-MS). Most of the important odorants were aldehydes and alcohols. For group A (veal calves fed entirely on milk for 90 d before slaughter), the most abundant compound class was the aldehydes (52.231%), while that was alcohols (26.260%) in group C (veal calves fed starter diet for at least 60 d before slaughter). In both classes the absolute percentages of the volatile compounds in veal were different indicating that the veal diet significantly (p<0.05) affected headspace volatile composition in veal as determined by principal component analysis (PCA). Twenty three volatile compounds showed significance by using a partial least-squared discriminate analysis (PLS-DA) (VIP>1). The establishment of the global volatile signature of veal may be a useful tool to define the beef diet that improves the organoleptic characteristics of the meat and consequently impacts both its taste and economic value.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3865-3883
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• 2016

Infrastructure as a Service (IaaS) encapsulates computer hardware into a large amount of virtual and manageable instances mainly in the form of virtual machine (VM), and provides rental service for users. Currently, VM anomaly incidents occasionally occur, which leads to performance issues and even downtime. This paper aims at detecting anomalous VMs based on performance metrics data of VMs. Due to the dynamic nature and increasing scale of IaaS, detecting anomalous VMs from voluminous correlated and non-Gaussian monitored performance data is a challenging task. This paper designs an anomaly detection framework to solve this challenge. First, it collects 53 performance metrics to reflect the running state of each VM. The collected performance metrics are testified not to follow the Gaussian distribution. Then, it employs independent components analysis (ICA) instead of principal component analysis (PCA) to extract independent components from collected non-Gaussian performance metric data. For anomaly detection, it employs multi-class Bayesian classification to determine the current state of each VM. To evaluate the performance of the designed detection framework, four types of anomalies are separately or jointly injected into randomly selected VMs in a campus-wide testbed. The experimental results show that ICA-based detection mechanism outperforms PCA-based and LDA-based detection mechanisms in terms of sensitivity and specificity.