• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1256-1256
• /
• 2001

The physico-chemical and texture characteristics of meat determine the nutritional, technological and sensory quality. However, the analysis of meat quality requires expensive, laborious and time consuming analytical methods. The objective of this study was to evaluate NIR spectroscopy using transmittance for determining the moisture, fat, protein and total pigment content, the water holding capacity (WHC) and the toughness of beef meat. A total of 318 spectra were recorded from ground beef samples by a Feed Analyzer 1265 of Infratec. The samples were obtained from the Longissimus muscle of the 10$^{th}$ rib of yearling bulls, ground with an electrical chopper, vacuum packaged, aged during 7 days and frozen at -24$^{\circ}C$ until the analyses were done. Moisture content was measured by oven drying at 10$0^{\circ}C$, fat content was determined by Soxhlet extraction and protein content was estimated from nitrogen content using the Kjeldahl analysis. The total pigment content was determined by the method of Hornsey and the WHC using the method of filter paper press. The instrumental evaluation of texture (maximum load WB, maximum stress MS and toughness) was conducted in an Instron equipment with a Warner-Bratzler shearing device. This analysis was performed on a chop of 3.5 cm obtained from the longissimus of the 8$^{th}$ rib, aged during 7 days, kept frozen at -24$^{\circ}C$ and cooked before the analysis. Near infrared spectra were recorded as log 1/T (T=transmittance) at 2 nm intervals from 850 to 1050 nm using a Feed Analyzer 1265 of Infratec. Calibrations were performed with the WinISI software (vs. 1.02) using the MPLS method. To examine the effect of scatter correction o. derivation of spectra on the calibration performance, calibrations were calculated with the crude spectra or pretreated with different mathematical treatments (inverse MSC, SNVD) and/or second derivative operation. For chemical composition, the use of the scatter corrections improved the calibration statistics, in terms of lower SECV and higher $r^2$. In most of the variables, the use of the 2$^{nd}$ derivative improved the predictions, mainly when combined with the SNVD treatment. However, for predicting the texture traits, the best estimation was obtained from the crude spectrum. These results showed that the equations obtained for predicting moisture, fat and total pigments were very accurate, with $r^2$ being higher that 0.9. However, the prediction of the texture traits (WB, MS, toughness) from ground meat was poor.

• Journal of Korea Water Resources Association
• /
• v.41 no.3
• /
• pp.325-340
• /
• 2008

This study employs Bayesian multiple regression analysis using the ordinary least squares method for regional low flow frequency analysis. The parameter estimates using the Bayesian multiple regression analysis were compared to conventional analysis using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian analysis at each return period are not significantly different. However, the difference between upper and lower limits is remarkably reduced using the Bayesian multiple regression. Therefore, from the point of view of uncertainty analysis, Bayesian multiple regression analysis is more attractive than the conventional method based on a t-distribution because the low flow sample size at the site of interest is typically insufficient to perform low flow frequency analysis. Also, we performed low flow prediction, including confidence interval, at two ungauged catchments in the Nakdong River basin using the developed Bayesian multiple regression model. The Bayesian prediction proves effective to infer the low flow characteristic at the ungauged catchment.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.1C
• /
• pp.65-76
• /
• 2010

Recent earthquake events revealed that severe seismic damages were concentrated mostly at sites composed of soil sediments rather than firm rock. This indicates that the site effects inducing the amplification of earthquake ground motion are associated mainly with the spatial distribution and dynamic properties of the soils overlying bedrock. In this study, an integrated GIS-based information system for geotechnical data was constructed to establish a regional counterplan against ground motions at a representative metropolitan area, Seoul, in Korea. To implement the GIS-based geotechnical information system for the Seoul area, existing geotechnical investigation data were collected in and around the study area and additionally a walkover site survey was carried out to acquire surface geo-knowledge data. For practical application of the geotechnical information system used to estimate the site effects at the area of interest, seismic zoning maps of geotechnical earthquake engineering parameters, such as the depth to bedrock and the site period, were created and presented as regional synthetic strategy for earthquake-induced hazards prediction. In addition, seismic zonation of site classification was also performed to determine the site amplification coefficients for seismic design at any site and administrative sub-unit in the Seoul area. Based on the case study on seismic zonations for Seoul, it was verified that the GIS-based geotechnical information system was very useful for the regional prediction of seismic hazards and also the decision support for seismic hazard mitigation particularly at the metropolitan area.

• The Journal of the Korea Contents Association
• /
• v.8 no.3
• /
• pp.252-263
• /
• 2008

This study was conducted to derive the optimal regionalization of the precipitation data which can be classified on the basis of climatologically and geographically homogeneous regions all over the regions except Cheju and Ulreung islands in Korea. A total of 65 rain gauges were used to regional analysis of precipitation. Annual maximum series for the consecutive durations of 1, 3, 6, 12, 24, 36, 48 and 72hr were used for various statistical analyses. K-means clustering mettled is used to identify homogeneous regions all over the regions. Five homogeneous regions for the precipitation were classified by the K-means clustering. Using the L-moment ratios and Kolmogorov-Smirnov test, the underlying regional probability distribution was identified to be the generalized extreme value (GEV) distribution among applied distributions. The regional and at-site parameters of the generalized extreme value distribution were estimated by the linear combination of the probability weighted moments, L-moment. The regional and at-site analysis for the design rainfall were tested by Monte Carlo simulation. Relative root-mean-square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE were computed and compared with those resulting from at-site Monte Carlo simulation. All show that the regional analysis procedure can substantially reduce the RRMSE, RBIAS and RR in RRMSE in the prediction of design rainfall. Consequently, optimal design rainfalls following the regions and consecutive durations were derived by the regional frequency analysis.

• Asian-Australasian Journal of Animal Sciences
• /
• v.17 no.12
• /
• pp.1736-1740
• /
• 2004

Since the application of relatively high levels of heavy metals in the compost poses a potential hazard to plants and animals, the content of heavy metals in the compost with animal manure is important to know if it is as a fertilizer. Measurement of heavy metals content in the compost by chemical methods usually requires numerous reagents, skilled labor and expensive analytical equipment. The objective of this study, therefore, was to explore the application of near-infrared reflectance spectroscopy (NIRS), a nondestructive, cost-effective and rapid method, for the prediction of heavy metals contents in compost. One hundred and seventy two diverse compost samples were collected from forty-seven compost facilities located along the Han river in Korea, and were analyzed for Cr, As, Cd, Cu, Zn and Pb levels using inductively coupled plasma spectrometry. The samples were scanned using a Foss NIRSystem Model 6500 scanning monochromator from 400 to 2,500 nm at 2 nm intervals. The modified partial least squares (MPLS), the partial least squares (PLS) and the principal component regression (PCR) analysis were applied to develop the most reliable calibration model, between the NIR spectral data and the sample sets for calibration. The best fit calibration model for measurement of heavy metals content in compost, MPLS, was used to validate calibration equations with a similar sample set (n=30). Coefficient of simple correlation (r) and standard error of prediction (SEP) were Cr (0.82, 3.13 ppm), As (0.71, 3.74 ppm), Cd (0.76, 0.26 ppm), Cu (0.88, 26.47 ppm), Zn (0.84, 52.84 ppm) and Pb (0.60, 2.85 ppm), respectively. This study showed that NIRS is a feasible analytical method for prediction of heavy metals contents in compost.

• Journal of the Korean GEO-environmental Society
• /
• v.17 no.6
• /
• pp.13-21
• /
• 2016

Since the landslide hazard areas prediction was analyzed by slope-angle and soil properties, regional characteristics is not taken. Therefore, in order to make more rational prediction, it is necessary to consider the characteristics of the region. Tree roots have been known to increase soil cohesion in landslide hazard areas and to vary the degrees depending on the tree type. In addition, a reasonable prediction of landslide hazard areas can be made by considering crown density based on crown distribution patterns of the area of interest. In this study, using the roots cohesion considering the crown density of the trees, which is in the landslides risk areas around Mt. Gwehwa in Sejong City, the landslides risk areas were predicted and compared with predicted results obtained by not considering root cohesion.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.2
• /
• pp.80-92
• /
• 2023

As cities are becoming densely populated, previously unexpected events such as crimes, accidents, and infectious diseases are bound to affect user demands. With a time-series prediction of demand using information with uncertainty, it is impossible to derive reliable results. In particular, the COVID-19 outbreak in early 2020 caused changes in abnormal travel patterns and made it difficult to predict demand for time series. A methodology that accurately predicts demand by detecting and reflecting these changes is, therefore, required. The current study suggests a time series modeling pipeline that automatically detects and predicts abnormal events caused by COVID-19. We expect its wide application in various situations where there is a change in demand due to irregular and abnormal events.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1269-1269
• /
• 2001

The Bovine Spongiform Encephalopathy (BSE) is one of the more important problems that have affected the economy of european cattles and the Public Safety. Their transmission is mainly through digestive system, and the compound feeds made with animal proteins are one source of infection for healthy cows. Nowadays the official method for meat and bone meal (MBM) detection in compound feeds is a microscopy technique. However, this methodology is subjective, and that alter the fact to make one exhaustive quantitative analysis and one differentiation between mammalian and poultry bones. In addition, the separation of the differents fractions in a sample by density before the analysis, requires the use of organochlorates products as $CCl_4$, which produce serious damages in the atmosphere ozone content. NIR methodology is another possible way to confirm and identifying animal ingredients in compound feeds, Its capabilities for quantitative and qualitative analysis of foods and feeds has been enought demonstrated. The objective of this work was to use NIR as a tool to make an qualitative and quantitative analysis and a prediction of the meat and bone meal presence in compound feeds from North Spain cattle farms. Using a global population of compound feeds, on make three different groups depending of MBM percentage presence (0, 0-100, 100), to build and validate one calibration equation to determine MBM content and make one discriminant analysis between these three groups. The preliminary dates obtained with another differents samples of known composition showed promising results.

• Journal of the Korean Geographical Society
• /
• v.45 no.3
• /
• pp.319-341
• /
• 2010

The purposes of this study are to classify heavy snowfall types in the Republic of Korea based on fresh snowfall data and atmospheric circulation data during the last 36(1973/74-2008/09) snow seasons and to identify typical surface synoptic climate patterns that characterize each heavy snowfall type. Four synoptic climate categories and seventeen regional heavy snowfall types are classified based on sea level pressure/surface wind vector patterns in East Asia and frequent spatial clustering patterns of heavy snowfall in the Republic of Korea, respectively. Composite analyses of multiple surface synoptic weather charts demonstrate that the locations and intensity of pressure/wind vector mean and anomaly cores in East Asia differentiate each regional heavy snowfall type in Korea. These differences in synoptic climatic fields are primarily associated with the surge of the Siberian high pressure system and the appearance of low pressure systems over the Korean Peninsula. In terms of hemispheric atmospheric circulation, synoptic climatic patterns in the negative mode of winter Arctic Oscillation (AO) are also associated with frequent heavy snowfall in the Republic of Korea at seasonal scales. These results from long-term synoptic climatic data could contribute to improvement of short-range or seasonal prediction of regional heavy snowfall.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.51 no.4
• /
• pp.7-13
• /
• 2009

The paddy irrigation demand for Nakdong river basin in Korea due to the climate change have been analyzed using regional climate model outputs. High-resolution (27 ${\times}$ 27 km) climate data for SRES A2 scenario produced by the Meteorological Research Institute (METRI), South Korea, and the observed baseline climatology dataset (1971-2000) were used. The outputs from the ECHO-G GCM model were dynamically downscaled using the MM5 regional model by METRI. Maps showing the predicted spatial variations of changes in climate parameters and paddy irrigation requirements have been produced using the geographic information system. The results of this study showed that the average growing season temperature will increase steadily by 1.5 $^{\circ}C$ (2020s A2), 3.2 $^{\circ}C$ (2050s A2) and 5.2 $^{\circ}C$ (2080s A2) from the baseline (1971-2000) 19.8 $^{\circ}C$. The average growing season rainfall will change by -3.4 % (2020s A2), 0.0 % (2050s A2) and +16.5 % (2080s A2) from the baseline value 886 mm. Assuming paddy area and cropping pattern remain unchanged the average volumetric irrigation demands were predicted to increase by 5.3 % (2020s A2), 8.1 % (2050s A2) and 2.2 % (2080s A2) from the baseline value 1.159 ${\times}$ $10^6\; m^3$. These projections are different from the previous study by Chung (2009) which used a different GCM and downscaling method and projected decreasing irrigation demands. This indicates that one should be careful in interpreting the results of similar studies.