• Korean Journal of Plant Resources
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• v.32 no.1
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• pp.29-37
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• 2019

This study was conducted to provide a basic data such as leaf morphological characteristics, total 110 individual trees selected from 11 wild population, for selective breeding. As a result of investigation of the twenty morphological characteristics of the leaf, there were statistically significant differences in all leaf characteristics among the populations. Especially, Mt. Mani population had larger leaf length (LL), width (LW) and area (LAR) than other populations. On the other hand, Mt. Beakwoon and Mt. Duryun had smaller leaf size (LL, LW and LAR) among the populations. Its principal component analysis (PCA) results showed that it represented 72.9% accumulated explanation from three principal component. The characteristics such as leaf area, leaf length and leaf width were highly contributed for classification among populations. According to the cluster analysis, the natural S. alnifolia populations were classified into five groups and Mt. Mani population was different from the other population.

• Applied Chemistry for Engineering
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• v.33 no.6
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• pp.563-573
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• 2022

This work aimed to develop a new equation for turbidity (Turb) simulation and prediction using statistical methods based on principal component analysis (PCA) and multiple linear regression (MLR). For this purpose, water samples were collected monthly over a five year period from Cheurfa dam, an important reservoir in Northwestern Algeria, and analyzed for 12 parameters, including temperature (T°), pH, electrical conductivity (EC), turbidity (Turb), dissolved oxygen (DO), ammonium (NH₄⁺), nitrate (NO₃^-), nitrite (NO₂^-), phosphate (PO₄^3-), total suspended solids (TSS), biochemical oxygen demand (BOD₅) and chemical oxygen demand (COD). The results revealed a strong mineralization of the water and low dissolved oxygen (DO) content during the summer period. High levels of TSS and Turb were recorded during rainy periods. In addition, water was charged with phosphate (PO₄^3-) in the whole period of study. The PCA results revealed ten factors, three of which were significant (eigenvalues >1) and explained 75.5% of the total variance. The F1 and F2 factors explained 36.5% and 26.7% of the total variance, respectively and indicated anthropogenic pollution of domestic agricultural and industrial origin. The MLR turbidity simulation model exhibited a high coefficient of determination (R² = 92.20%), indicating that 92.20% of the data variability can be explained by the model. TSS, DO, EC, NO₃^-, NO₂^-, and COD were the most significant contributing parameters (p values << 0.05) in turbidity prediction. The present study can help with decision-making on the management and monitoring of the water quality of the dam, which is the primary source of drinking water in this region.

• Korean Journal of Environmental Biology
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• v.40 no.1
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• pp.70-86
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• 2022

A survey was conducted to analyze seasonal dynamics of the phytoplankton community at 22 stations on the surface and bottom layers in the Anma Islands of Yeonggwang(AIY) in the southern West Sea, Korea from the spring of 2020 to the winter of 2021, using a marine survey vessel Ed Ocean. Based on the survey results, there were 87 phytoplankton species in 52 genera, diatoms accounted for 67.8%, dinoflagellates 26.5%, silicoflagellates 3.5%, and cryptomonads and euglenoids accounted for 1.1% each. By season, it was simple in spring and relatively varied in winter. The phytoplankton standing crop on the surface was low (28.8±30.1 cells mL^-1) in summer and high (87.0±65.1 cells mL^-1) in spring. In the phytoplankton community, diatoms showed a high share (over 80%) throughout the year, and Skeletonema costatum-ls was the dominant species with a dominance of more than 60% in spring and winter, and 34.6% and 24.2% in summer and autumn, respectively. The diversity expressing the characteristics of the community structure was high (2.79±0.45) in autumn and low (1.82±0.18) in spring, unlike the phytoplankton standing crop. However, the dominance was high at (0.86±0.08) in spring and low (0.44j0.13) in autumn. Based on the results of principal component analysis (PCA) using environmental and phytoplankton-related factors, it was estimated that the biological oceanographic environmental characteristics seen through the phytoplankton community in the AIY were dominated by nutrients supplied from open seawater and surface sediments by seawater mixing, such as tidal mixing.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.173-178
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• 2024

This study presents a technique for assessing the dimensional quality of assembly parts in Prefabricated Steel Structures (PSS) using a stereo vision sensor. The stereo vision system captures images and point cloud data of the assembly area, followed by applying image processing algorithms such as fuzzy-based edge detection and Hough transform-based circular bolt hole detection to identify bolt hole locations. The 3D center positions of each bolt hole are determined by correlating 3D real-world position information from depth images with the extracted bolt hole positions. Principal Component Analysis (PCA) is then employed to calculate coordinate axes for precise measurement of distances between bolt holes, even when the sensor and structure orientations differ. Bolt holes are sorted based on their 2D positions, and the distances between sorted bolt holes are calculated to assess the assembly part's dimensional quality. Comparison with actual drawing data confirms measurement accuracy with an absolute error of 1mm and a relative error within 4% based on median criteria.

• Korean Journal of Mineralogy and Petrology
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• v.37 no.2
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• pp.23-34
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• 2024

It is essential to know the mineral composition of core samples to assess the possibility of gas hydrate (GH) in sediments. During the exploration of gas hydrates (GH), mineral composition values were obtained from each core sample collected in the Ulleung Basin using X-ray diffraction (XRD). Based on this data, machine learning was performed with 3100 input values representing XRD peak intensities and 12 output values representing mineral compositions. The 488 data points were divided into 307 training samples, 132 validation samples, and 49 test samples. The random forest (RF) algorithm was utilized to obtain results. The machine learning results, compared with expert-predicted mineral compositions, revealed a Mean Absolute Error (MAE) of 1.35%. To enhance the performance of the developed model, principal component analysis (PCA) was employed to extract the key features of XRD peaks, reducing the dimensionality of input data. Subsequent machine learning with the refined data resulted in a decreased MAE, reaching a maximum of 1.23%. Additionally, the efficiency of the learning process improved over time, as confirmed from a temporal perspective.

• Journal of Microbiology and Biotechnology
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• v.20 no.12
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• pp.1702-1710
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• 2010

Korean traditional rice wines yakju and takju are generally brewed with nuruk as the source of the saccharogenic enzymes by natural fermentation. To improve the quality of Korean rice wine, the microorganisms in the nuruk need to be studied. The objective of this research was to improve the quality of Korean wine with the wild-type yeast strains isolated from the fermentation starter, nuruk. Only strain YA-6 showed high activity in 20% ethanol. Precipitation of Y89-5-3 was similar to that of very flocculent yeast (>80%) at 75.95%. Using 18S rRNA sequencing, all 10 strains were identified as Saccharomyces cerevisiae. Volatile compounds present in yakju were analyzed by gas chromatography-mass selective detector. The principal component analysis (PCA) of the volatile compounds grouped long-chain esters on the right side of the first principal component, PC1; these compounds were found in yakju that was made with strains YA-6, Y89-5-3, Y89-5-2, Y90-9, and Y89-1-1. On the other side of PC1 were short-chain esters; these compounds were found in wines that were brewed with strains Y183-2, Y268-3, Y54-3, Y98-4, and Y88-4. Overall, the results indicated that using different wild-type yeast strains in the fermentation process significantly affects the chemical characteristics of the glutinous rice wine.

• Journal of the Korean Society of Food Science and Nutrition
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• v.36 no.7
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• pp.874-880
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• 2007

Volatile flavor compounds of 4 grape species (Campbell, Sheridan, Red globe, and Meoru) were identified during 3-day storage at either $4^{\circ}C$ or room temperature. Each sample was analyzed by solid-phase micro-extraction (SPME) method combined with gas chromatography-mass spectrometry. Also electronic nose composed of 12 different metal oxide sensors was used to differentiate flavors of grapes. Sensitivities (delta $R_{gas}/R_{air}$) of sensors from electronic nose were obtained by principal component analysis (PCA). Proportion of the first principal component was 99.30% at $4^{\circ}C$ and 99.36% at room temperature, respectively. In our result, flavor patterns of grape can be differentiated according to the storage period. The major volatile flavor compounds were 1-hexanol, hexanoic acid and its ethyl ester, and phenylethyl alcohol with the presence of butanoic acid and its ethyl ester, acetic acid, benzeneacetic acid and its ethyl ester.

• Korean Journal of Plant Taxonomy
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• v.42 no.2
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• pp.126-140
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• 2012

We conducted principal component analyses using the thirty two quantitative characteristics of Najas from South Korea to examine the morphological variation and diagnostic characteristics. As a result of our investigation and the morphometric analyses, each taxon could be identified using the leaf width, shape of the leaf sheath, number of teeth on the leaf margin, number of anther cells, number of areoles in each longitudinal row of seeds, and the ratio of the areole width to the length. Dioecious Najas marina was clearly distinguished from the other monoecious taxa (N. graminea, N. gracillima, N. minor, N. oguraensis, and N. orientalis) by the larger size of the stem, the leaf, the flower and the fruit. The monoecious taxa could be identified using vegetative characteristics except for N. minor and N. oguraensis, which were distinct from each other according to the locule number in the staminate flower.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.20-26
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• 2008

As they have more and more intelligence robots or computers these days, so the interaction between intelligence robot(computer) - human is getting more and more important also the emotion recognition and expression are indispensable for interaction between intelligence robot(computer) - human. In this paper, firstly we extract emotional features at speech signal and facial image. Secondly we apply both BL(Bayesian Learning) and PCA(Principal Component Analysis), lastly we classify five emotions patterns(normal, happy, anger, surprise and sad) also, we experiment with decision fusion and feature fusion to enhance emotion recognition rate. The decision fusion method experiment on emotion recognition that result values of each recognition system apply Fuzzy membership function and the feature fusion method selects superior features through SFS(Sequential Forward Selection) method and superior features are applied to Neural Networks based on MLP(Multi Layer Perceptron) for classifying five emotions patterns. and recognized result apply to 2D facial shape for express emotion.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.6_spc
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• pp.714-720
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• 2016

Rubbing and misalignment are the most usual faults that occurs in rotating machinery and with them severe effect on power plant availability. Especially blade rubbing is hard to detect on FFT spectrum using the vibration signal. In this paper, the possibility of feature analysis of vibration signal is confirmed under blade rubbing and misalignment condition. And the lab-scale rotor test device provides the blade rubbing and shaft misalignment modes. Feature selection based on GA (genetic algorithm) is processed by the extracted feature of the time domain. Then, classification of the features is analyzed by using SVM (support vector machine) which is one of the machine learning algorithm. The results of features selection based on GA compared with those based on PCA (principal component analysis). According to the results, the possibility of feature analysis is confirmed. Therefore, blade rubbing and shaft misalignment can be diagnosed by feature of vibration signal.