• Journal of Sensor Science and Technology
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• v.22 no.4
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• pp.286-291
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• 2013

This paper presents to analyze patterns from single and complex malodors using gas sensor array based on metal oxide semiconductors. The aim of research is to identify and discriminate single malodors such as $NH_3$, $CH_3SH$ and $H_2S$ and their mixtures according to concentration levels. Measurement system for analyzing patterns from malodors is constructed by an array of metal oxide semiconductor sensors which are available commercially together with associate electronics. The patterns from sensory system are analyzed by Principal Component Analysis (PCA) which is simple statistical pattern recognition technique. Throughout the experimental trails, we confirmed the experimental procedure for measurement system such as sensors calibration time and gas flow rate, and discriminated malodors using pattern analysis technique.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.473-473
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• 2010

Plasma is widely used in various commercial etchers and chemical vapor deposition. Unfortunately, real-time plasma process monitoring is still difficult. Some methods of plasma diagnosis is improved, however, it is possible for real-time plasma diagnosis to use non-intrusive probe only. In this research, the object is to investigate the suitability of using impedance analysis and optical emission spectroscopy (OES) for real-time plasma process monitoring. It is assumed that plasma system is a equivalent circuit. Therefore, V-I probe is used for measuring impedance, which can be a new non-intrusive probe for plasma diagnosis. From impedance data, we tried to analyse physical properties of plasma. And OES, the other method of plasma diagnosis, is a typical non-intrusive probe for analyzing chemical properties. The amount of the OES data is typically large, so this poses a difficulty in extracting relevant information. To solve this problem, principal component analysis (PCA) can be used. For fundamental information, Ar plasma and $O_2$ plasma are used in this experiment. This method can be applied to real-time endpoint and fault detections.

• Journal of the Korean Society of Food Culture
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• v.31 no.6
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• pp.624-633
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• 2016

This study identified the sensory properties of samples of pizza dough at three pizza companies and three masonry oven pizzerias from Seoul, Korea and compared consumer acceptability among panels of university students. Six pizza dough samples were prepared (pan pizzas from Pizza Hut, Mr.pizza, and Dominos pizza, masonry oven baked pizzas from Appleteen, Mr.Lee's, and Pizza factory). Consumer tests were employed involving 97 Korean consumers. Consumers evaluated overall liking (OL), liking of appearance (APPL), odor (ODL), flavor (FLL), and texture (TXTL), willing to try (WT), and willing to recommend (WR) for the samples using a nine-point hedonic scale. Analysis of variance (ANOVA) indicated that HutP, MrP, and DomP samples had significantly (p<0.05) high scores for roughness, porosity, crust color, grain size, brownness, dairy food aroma, savory taste, and yeast aroma, which had the highest OL, ODL, and FLL scores. LeeP, ATeenP, and PFacP samples had high elasticity, cohesiveness, and adhesiveness. Consumers favored the appearance characteristics and color, dairy product flavor, and savory flavor of the pan pizza and preferred cohesiveness, toughness, and stickiness of masonry oven baked pizza.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2239-2246
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• 2024

The present study aimed to assess the radioactive hazards associated with the application of granitoid rocks in building materials. An HPGe spectrometer was used to detect the levels of the radioactive elements uranium-238, thorium-232, and potassium-40 in the granitoid rocks. The results showed that the levels of these elements were lower (38.32 < 33 Bq kg^-1), comparable (47.19-45 Bq kg^-1) and higher (992.26 ≫> 412 Bq kg^-1) than the worldwide limits for ²³⁸U, ²³²Th, and ⁴⁰K concentration, respectively. The exposure to gamma radiation of granitoid rocks was studied by various radiological hazard variables like the absorbed dose rate (D_air), the outdoor and indoor annual effective dose (AED_out and AED_in), and excess lifetime cancer risk (ELCR). A variety of statistical methods, including Pearson correlation, principal component analysis (PCA), and hierarchical cluster analysis (HCA) was used, to study the relationship between the radioactive elements and the radiological hazards. According to statistical analysis, the main radioactive risk of granitoid rocks is contributed to by the elements uranium-238, thorium-232, and potassium-40. Granitoid rocks can be applied in building materials, but under control to prevent risk to the public.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.303-303
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• 2022

The purpose of this study was to evaluate agronomic characteristics and identify the useful traits to utilize the wheat genetic resources for breeding programs by understanding the phenotypic variation among germplasm through multivariate analysis. In this study, a total of 394 wheat accessions were characterized for 15 agronomic traits using the National Agrobiodiversity Center (NAC) descriptor list, of which 31 accessions from 6 species and 363 unidentified accession (Triticum spp.) available at the NAC, Rural Development Administration (RDA), Korea. Growth characteristics such as leaf width, culm length, spike length, spikelet length, solid stemmed, days to heading, days to maturity, grain-filing period, and also seed characteristics such as width, height, area, perimeter, circle, solidity, and germination percent were studied. Among the 15 agronomic characteristics, the germination percent showed the smallest variation between resources (CV = 0.4%), and the spikelet length (CV = 66.5%) showed the highest variation. A strong positive correlation was found between seed traits such as seed height and seed area (r = 0.90), seed height and seed perimeter (r = 0.87) and seed length and width (r = 0.80). Principal component analysis (PCA) was conducted and the first five principal components comprised 76.7% of the total variance. Among the first five PCs, PCI accounted for 28.5% and PC2 for 20.0%. Wheat resources (394) were classified into four clusters based on cluster analysis, consisting of 215 resources(I), 117 resources(II), 48 resources(III), and 14 resources(IV). Among the clusters, the resources belonging to Cluster III showed the lowest seed width, height, area, and perimeter characteristics compared to other clusters. The wheat resources belonging to cluster IV had small seed width and low germination percent, but took longer to form heads and mature than resources in other clusters. These results will serve as the basis for further genetic diversity studies, and important agronomic characteristics will be used for improving wheat, including developing high-yielding and resistant varieties to biotic and abiotic stresses via breeding programs.

• Food Science of Animal Resources
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• v.24 no.3
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• pp.260-265
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• 2004

This study was carried out to investigate aroma patterns of Hanwoo (Korean cattle) beef using electronic nose during refrigerated storage, and to compare these results with chemical quality (pH, TBARS). The M. longissimus muscle from Hanwoo carcasses after 24 hrs postmortem was obtained and stored at 5${\pm}$1$^{\circ}C$ for 7 days. Sensitivity (dR/RO) values among electronic nose data were changed differently during refrigerated storage, and showed significant difference on the 7th day of storage (p<0.01). The dR/RO from SY/G, SY/AA, SY/Gh, SY/gCTl, SY/gCT decreased but those from SY/LG, T30/1, P10/1, P10/2, P40/1, T70/2, PA2 increased during storage for 7 days. Mapping these data using PCA (principal component analysis) showed that the 1st day data were present in the middle of the right side, the 3rd day data were present in bottom part of this area and the 7th day data spread out more widely on the left side. In case of DFA (discriminant factor analysis), the flock clustered round and located in different side clearly comparing with PCA plot. In analysis of correlation coefficients among electronic nose data and chemical quality data, there was significant correlation among sensor data (p<0.001). But pH and TBARS were not significantly correlated with electronic nose data. Consequently, PCA and DFA plot by electronic nose data showed difference during refrigerated storage and there were significant correlations among sensors. Therefore it will be possible to detect separate aroma patterns of Hanwoo beef using electronic nose.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1859-1867
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• 2021

This study investigated seasonal characteristics of the particulate matter (PM) in the atmosphere and rainwater quality in Busan, South Korea, and evaluated the seasonal effect of PM₁₀ concentration in the atmosphere on the rainwater quality using multivariate statistical analysis. The concentration of PM in the atmosphere and meteorological observations(daily precipitation amount and rainfall intensity) are obtained from automatic weather systems (AWS) by the Korea Meteorological Administration (KMA) from March 2020 to August 2020. Rainwater samples (n = 216, 13 rain events) were continuously collected from the beginning of the precipitation using the rainwater collecting device at Pukyong National University. The samples were analyzed for pH, EC (electrical conductivity), water-soluble cations(Na⁺, Mg²⁺, K⁺, Ca²⁺, and NH₄⁺), and anions(Cl^-, NO₃^-, and SO₄^2-). The concentration of PM₁₀ in the atmosphere was steadily measured before and after the precipitation with a custom-built PM sensor node. The measured data were analyzed using principal component analysis (PCA) and Pearson correlation analysis to identify relationships between the concentration of PM₁₀ in the atmosphere and rainwater quality. In spring, the daily average concentration of PM₁₀ (34.11 ㎍/m³) and PM_2.5 (19.23 ㎍/m³) in the atmosphere were relatively high, while the value of daily precipitation amount and rainfall intensity were relatively low. In addition, the concentration of PM₁₀ in the atmosphere showed a significant positive correlation with the concentration of water-soluble ions (r = 0.99) and EC (r = 0.95) and a negative correlation with the pH (r = -0.84) of rainwater samples. In summer, the daily average concentration of PM₁₀ (27.79 ㎍/m³) and PM_2.5 (17.41 ㎍/m³) in the atmosphere were relatively low, and the maximum rainfall intensity was 81.6 mm/h, recording a large amount of rain for a long time. The results indicated that there was no statistically significant correlation between the concentration of PM₁₀ in the atmosphere and rainwater quality in summer.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1181-1181
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• 2001

Robinson with ${coworkers}^{1}$ have introduced two-state outer-neighbor bonding model to explain the anomalies of water. The studies on the properties of water as a function of temperature and pressure revealed that, unlike other ideas, all $H_2O$ molecules in liquid are tetrabonded. On the average they are forming two different bonding types. One type is the regular tetrahedral water-water bonding similar to that found in the ordinary ice Ih, whereas the other is a more dense nonregular tetrahedral bonding similar to that appearing in the ice II. The transformation between these two bonding forms is evidenced by FT-NIR experiment. The FT-NIR measurements were done for liquid water in the temperature range from $20^{\circ}C$ up to $80^{\circ}C$ in a wide extent of frequencies: 12 000 - 4000 $cm^{-1}$ /. Temperature dependent variations in the volume fraction of these two structures are directly related to the spectral changes. The absorbance variations are explored by means of the two-dimensional correlation spectroscopy (2DCOS), principal component analysis (PCA), curve fitting and second derivatives. The presence of the isosbestic points in a range of the combination and overtone transitions indicates that the experimental spectra are a superposition of two temperature independent components. One component of diminishing intensity with temperature increase, is assigned to a stronger hydrogen bonds occurred in the Ih type, whereas the second component showing an opposite behavior, one can attribute to a weaker H-bonds characteristic for the II type. The understanding of the hydrogen bonding network in the liquid water is very important in interpretation of the interaction between water and protein chain. The two-state model of water surrounding the protein surface could advance an understanding of the hydration process.

• Korean Chemical Engineering Research
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• v.45 no.6
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• pp.596-603
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• 2007

Rapid or on-line coal analysis is of great interest in coal industry as it would allow efficient plant operation. Multivariate analysis has been applied to near-infrared(NIR) spectra coal for investigating the relationship between coal properties(%) (moisture, ash, volatile matter, fixed carbon, carbon, hydrogen, nitrogen, oxygen, sulfur), heating value(kcal/kg) and corresponding near-infrared spectral data. The quantitative analysis was carried out by applying PLS(partial least squares regression) to determine a methodology able to establish a relationship between coal properties and NIR spectral data being applied mathematical pre-treatments for minimizing the physical features of the samples. As a results of the analysis, this technique is able to classify the species of coals and to predict the all coal properties except ash, nitrogen and sulfur. The efficient operation of coal fired power plant is expected owing to real time on-line coal analysis of moisture and heating value.