• Journal of Environmental Science International
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• v.16 no.12
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• pp.1411-1424
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• 2007

Long-term trends and distribution patterns of water quality were investigated in the Asan coastal areas of Yellow Sea, Korea from 1975 to 2005. Water samples were collected at 3 stations and physicochemical parameters were analyzed including water temperature, salinity, suspended solids(SS), chemical oxygen demand(COD), dissolved oxygen(DO) and nutrients. Spatial distribution patterns were not clear among stations but the seasonal variations were distinct except COD, SS and nitrate. The trend analysis by principal component analysis(PCA) during twenty years revealed the significant variations in water quality in the study area, Annual water qualities were clearly discriminated into 4 clusters by PCA; year cluster 1988-1991, 1994-1997, and 1992-1993/1998-2005. By this multi-variate analysis we can summarize the annual trends as the followings; salinity, suspended solids and dissolved oxygen tended to increase from late 1980's, increased pH and COD from 1992, and decreased salinity and increased nitrogen and COD from 1990 due to the runoff frow agricultural lands causing eutrophication.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.13-18
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• 2010

Today, The much amount of spam that occupies the mail server and network storage occurs the lack of negative issues, such as overload, and for users to delete the spam should spend time, resources have a problem. Automatic spam filtering on the incidence to solve the problem is essential. A lot of Spam filters have tried to solve the problem emerged as an essential element automatically. Unlike traditional method such as Naive Bayesian, PCA through the many-dimensional data set of spam with a few spindle-dimensional process that narrowed the operation to reduce the burden on certain groups for classification Logistic regression analysis method was used to filter the spam. Through the speed and performance, it was able to get the positive results.

• Computers and Concrete
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• v.23 no.6
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• pp.421-431
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• 2019

The alarming rate of depletion of natural stone based coarse aggregates is a cause of great concern. The coarse aggregates occupy nearly 60-70% by volume of concrete being produced. Research efforts are on to look for alternatives to stone based coarse aggregates from sustainability point of view. Response surface methodology (RSM) is adopted to study and address the effect of ferrochrome slag (FCS) replacement to coarse aggregate replacement in the ordinary Portland cement (OPC) based concretes. RSM involves three different factors (ground granulated blast furnace slag (GGBS) as binder, flyash (FA) as binder, and FCS as coarse aggregate), with three different levels (GGBS (0, 15, and 30%), FA (0, 15, and 30%) and FCS (0, 50, and 100%)). Experiments were carried out to measure the responses like, workability, density, and compressive strength of FCS based concretes. In order to optimize FCS replacement in the OPC based concretes, three different traditional optimization techniques were used (grey relational analysis (GRA), technique for order of preference by similarity (TOPSIS), and desirability function approach (DFA)). Traditional optimization techniques were accompanied with principal component analysis (PCA) to calculate the weightage of responses measured to arrive at the final ranking of replacement levels of GGBS, FA, and FCS in OPC based concretes. Hybrid combination of PCA-TOPSIS technique is found to be significant when compared to other techniques used. 30% GGBS and 50% FCS replacement in OPC based concrete was arrived at, to be optimal.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1562-1573
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• 2024

This study rigorously examined uncertainty in the TMI-1 benchmark within the Uncertainty Analysis in Modeling (UAM) benchmark suite using the STREAM/RAST-K two-step method. It presents two pivotal advancements in computational techniques: (1) Development of an uncertainty quantification (UQ) module and a specialized library for the pin-based pointwise energy slowing-down method (PSM), and (2) Application of Principal Component Analysis (PCA) for UQ. To evaluate the new computational framework, we conducted verification tests using SCALE 6.2.2. Results demonstrated that STREAM's performance closely matched SCALE 6.2.2, with a negligible uncertainty discrepancy of ±0.0078% in TMI-1 pin cell calculations. To assess the reliability of the PSM covariance library, we performed verification tests, comparing calculations with Calvik's two-term rational approximation (EQ 2-term) covariance library. These calculations included both pin-based and fuel assembly (FA-wise) computations, encompassing hot zero-power and hot full-power operational conditions. The uncertainties calculated using both the EQ 2-term and PSM resonance treatments were consistent, showing a deviation within ±0.054%. Additionally, the data compression process yielded compression ratios of 88.210% and 92.926% for on-the-fly and data-saving approaches, respectively, in TMI fuel assembly calculations. In summary, this study provides a comprehensive explanation of the PCA process used for UQ calculations and offers valuable insights into the robustness and reliability of newly developed computational methods, supported by rigorous verification tests.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.48 no.5
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• pp.413-418
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• 2003

Genetic diversity and soybean sprout-related traits were evaluated in a total of 72 soybean accessions (60 Glycine max, 7 Glycine soja, and 5 Glycine gracilis). 100-seed weight (SW) was greatly varied and ranged from 3.2g to 32.3g in 72 soybean accessions. Positive correlation was observed between GR and hypocotyl length (HL), whereas negative correlation was observed between SW and hypocotyl diameter (HD). Re-evaluation by discarding two soybean genotypes characterized with low GR indicated that much higher correlation of sprout yield (SY) with HD and SW. Based on the principal component analysis (PCA) for sprout-related traits, 57 accessions were classified. Soybean genotypes with better traits for sprout, such as small size of seeds and high SY, were characterized with high PCA 1 and PCA 2 values. The seed size in second is small but showed low GR and SY, whereas the third has large seed, high GR and more than 400% SY. In genetic similarity analysis using 60 SSR marker genotyping, 72 accessions were classified into three major and several minor groups. Nine of twelve accessions that were identified as the representatives of soybean for sprout based on PCA were in a group by the SSR marker analysis, indicating the SSR marker selection of parental genotypes for soybean sprout improvement program.

• Journal of the Korean Society of Food Culture
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• v.20 no.1
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• pp.53-60
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• 2005

This study was conducted to develop the descriptive analysis procedures for evaluating the sensory characteristics of yeast leavened breads. Eleven highly trained panelists identified the following 23 sensory attributes in the bread and defined the terminology for each attribute; yellowness of crumb, roughness of surface, uniformity of cell, density of cell, brownness of crust for appearance characteristics, yeast fermented, chemical, roasted flour, buttery, milky, boiled flour, sweet, and salty for flavor characteristics, springiness, ease to tear, moistness on surface, adhesiveness to lip, hardness, stickiness, cohesiveness of mass, moisture absorption, chewiness, and loose particles for textural characteristics. Reference samples for the flavor attributes were determined. There were significant differences in all of the 23 sensory attributes of commercial bread samples. The principal component analysis (PCA) was performed to summarize the sensory data. The first two principal components explained 89% of the variation of the original variables indicating reliability of procedure developed in this study.

• Journal of Physiology & Pathology in Korean Medicine
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• v.34 no.4
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• pp.159-169
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• 2020

Shanghanlun (Treatise on Cold Damage Diseases) is the oldest document in the literature on clinical records of Traditional Asian medicine (TAM), on which TAM theories about symptoms-herbs relationships are based. In this study, we aim to quantitatively explore the relationships between symptoms and herbs in Shanghanlun. The text in Shanghanlun was converted into structured data. Using the structured data, Term Frequency - Inverse Document Frequency (TF-IDF) scores of symptoms and herbs were calculated from each chapter to derive the major symptoms and herbs in each chapter. To understand the structure of the entire document, principal component analysis (PCA) was performed for the 6-dimensional chapter space. Bipartite network analysis was conducted focusing on Jaccard scores between symptoms and herbs and eigenvector centralities of nodes. TF-IDF scores showed the characteristics of each chapter through major symptoms and herbs. Principal components drawn by PCA suggested the entire structure of Shanghanlun. The network analysis revealed a 'multi herbs - multi symptoms' relationship. Common symptoms and herbs were drawn from high eigenvector centralities of their nodes, while specific symptoms and herbs were drawn from low centralities. Symptoms expected to be treated by herbs were derived, respectively. Using measurable metrics, we conducted a computational study on patterns of Shanghanlun. Quantitative researches on TAM theories will contribute to improving the clarity of TAM theories.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.47-57
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• 2010

The object detection is essential for identifying objects, location information, and user context-aware in the image. In this paper, we propose a robust object detection system. The System linearly transforms learning data obtained from the background images to Principal components. It organizes the Eigen-background with the selected Principal components which are able to discriminate between foreground and background. The Fuzzy-C-means (FCM) carries out clustering for images with inputs from the Eigen-background information and classifies them into objects and backgrounds. It used various patterns of backgrounds as learning data in order to implement a system applicable even to the changing environments, Our system was able to effectively detect partial movements of a human body, as well as to discriminate between objects and backgrounds removing noises and shadows without anyone frame image for fixed background.

• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.300-307
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• 2008

This paper is concerned with face recognition for human-robot interaction (HRI) in robot environments. For this purpose, we use Tensor Subspace Analysis (TSA) to recognize the user's face through robot camera when robot performs various services in home environments. Thus, the spatial correlation between the pixels in an image can be naturally characterized by TSA. Here we utilizes face database collected in u-robot test bed environments in ETRI. The presented method can be used as a core technique in conjunction with HRI that can naturally interact between human and robots in home robot applications. The experimental results on face database revealed that the presented method showed a good performance in comparison with the well-known methods such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) in distant-varying environments.

• Journal of Biomedical Engineering Research
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• v.24 no.4
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• pp.287-292
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• 2003

Even though MCG has many advantages over ECG, MCG signa)s are easily corrupted by external magnetic noises Since multi-channel MCG signals are recorded simultaneously at many spatial positions, it is effective to apply spatial fitters as well as the conventional temporal filters to remove external magnetic noises. The spatial filters can be designed by utilizing the fact that the noise signals caused by external noise sources are more spatially correlated than the original MCG signals. In this paper, we introduce a spatial filtering method for the noise reduction in MCG based on the principal component analysis. Healthy volunteer study results obtained with a 61-channel MCG system are presented.