• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5A
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• pp.673-683
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• 2008

This study improved the existing method of using the longitudinal strain and concept of influence line to develop Bridge Weigh-in-Motion system without axle detector using the dynamic strain of the bridge girders and concrete slab. This paper first describes the considered algorithms of extracting passing vehicle information from the dynamic strain signal measured at the bridge slab, girders, and cross beams. Two different analysis methods of 1) influence line method, and 2) neural network method are considered, and parameter study of measurement locations is also performed. Then the procedures and the results of field tests are described. The field tests are performed to acquire training sets and test sets for neural networks, and also to verify and compare performances of the considered algorithms. Finally, comparison between the results of different algorithms and discussions are followed. For a PSC I-girder bridge, vehicle weight can be calculated within a reasonable error range using the dynamic strain gauge installed on the girders. The passing lane and passing speed of the vehicle can be accurately estimated using the strain signal from the concrete slab. The passing speed and peak duration were added to the input variables to reflect the influence of the dynamic interaction between the bridge and vehicles, and impact of the distance between axles, respectively; thus improving the accuracy of the weight calculation.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.397-418
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• 2024

The Land Surface Temperature (LST) is a crucial parameter in identifying drought. It is essential to identify how LST can increase its accuracy, particularly in mountainous and hill areas. Increasing the LST accuracy can be achieved by applying early data processing in the correction phase, specifically in the context of topographic correction on the Lambertian model. Empirical evidence has demonstrated that this particular stage effectively enhances the process of identifying objects, especially within areas that lack direct illumination. Therefore, this research aims to examine the application of the Lambertian model in estimating LST using the Multi-Channel Method (MCM) across various physiographic regions. Lambertian model is a method that utilizes Lambertian reflectance and specifically addresses the radiance value obtained from Sun-Canopy-Sensor(SCS) and Cosine Correction measurements. Applying topographical adjustment to the LST outcome results in a notable augmentation in the dispersion of LST values. Nevertheless, the area physiography is also significant as the plains terrain tends to have an extreme LST value of ≥ 350 K. In mountainous and hilly terrains, the LST value often falls within the range of 310-325 K. The absence of topographic correction in LST results in varying values: 22 K for the plains area, 12-21 K for hilly and mountainous terrain, and 7-9 K for both plains and mountainous terrains. Furthermore, validation results indicate that employing the Lambertian model with SCS and Cosine Correction methods yields superior outcomes compared to processing without the Lambertian model, particularly in hilly and mountainous terrain. Conversely, in plain areas, the Lambertian model's application proves suboptimal. Additionally, the relationship between physiography and LST derived using the Lambertian model shows a high average R² value of 0.99. The lowest errors(K) and root mean square error values, approximately ±2 K and 0.54, respectively, were achieved using the Lambertian model with the SCS method. Based on the findings, this research concluded that the Lambertian model could increase LST values. These corrected values are often higher than the LST values obtained without the Lambertian model.

• Journal of Food Hygiene and Safety
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• v.28 no.1
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• pp.7-12
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• 2013

This study presents the influence on growth properties determined using a novel predictive growth model of wild-type Salmonella spp. KSC 101 by variations in the temperature and time during cut packaging in cold, uncooked pork meat. The experiment performed for model development included an arrangement of different temperatures ($0^{\circ}C$, $5^{\circ}C$, $10^{\circ}C$, $15^{\circ}C$, and $20^{\circ}C$) and time durations (0, 1, 2, and 3 hours) that reflect actual pork-cut and packaging processes. No growth was observed at $0^{\circ}C$ and $5^{\circ}C$, whereas some growth was observed at $10^{\circ}C$, $15^{\circ}C$, and $20^{\circ}C$, with a mean increase of only 0.34 log CFU/g. The growth observed at $20^{\circ}C$ was more robust than that observed at $15^{\circ}C$, but the difference was not statistically significant (p > 0.05). However, compared with PMP (Pathogen Modeling Program), the wild-type Salmonella spp. KSC 101 showed a more rapid growth. We used the Gompertz 4 parameter equation as the primary model, and the exponential decay formula as the secondary model. The estimated $R^2$ values were 0.99 or higher. The developed model was evaluated by comparison of the experimental and predictive values, and the values were in agreement with the ${\pm}0.5$ log CFU/g, although the RMSE (Root mean square error) value was 0.103, which indicates a slight overestimation. Therefore, we suggest that the developed predictive growth model would be useful as a tool for evaluating sanitation criteria in pork cut-packaging processes.

• Journal of Korean Society of Forest Science
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• v.90 no.2
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• pp.197-209
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• 2001

TOPMODEL, semi-distributed hydrological model, is frequently applied to predict the amount of discharge, main flow pathways and water quality in a forested catchment, especially in a spatial dimension. TOPMODEL is a kind of conceptual model, not physical one. The main concept of TOPMODEL is constituted by the topographic index and soil transmissivity. Two components can be used for predicting the surface and subsurface contributing area. This study is conducted for the validation of applicability of TOPMODEL at small forested catchments in Korea. The experimental area is located at Gwangneung forest operated by Korea Forest Research Institute, Gyeonggi-do near Seoul metropolitan. Two study catchments in this area have been working since 1979 ; one is the natural mature deciduous forest(22.0 ha) about 80 years old and the other is the planted young coniferous forest(13.6 ha) about 22 years old. The data collected during the two events in July 1995 and June 2000 at the mature deciduous forest and the three events in July 1995 and 1999, August 2000 at the young coniferous forest were used as the observed data set, respectively. The topographic index was calculated using $10m{\times}10m$ resolution raster digital elevation map(DEM). The distribution of the topographic index ranged from 2.6 to 11.1 at the deciduous and 2.7 to 16.0 at the coniferous catchment. The result of the optimization using the forecasting efficiency as the objective function showed that the model parameter, m and the mean catchment value of surface saturated transmissivity, $lnT_0$ had a high sensitivity. The values of the optimized parameters for m and InT_0 were 0.034 and 0.038; 8.672 and 9.475 at the deciduous and 0.031, 0.032 and 0.033; 5.969, 7.129 and 7.575 at the coniferous catchment, respectively. The forecasting efficiencies resulted from the simulation using the optimized parameter were comparatively high ; 0.958 and 0.909 at the deciduous and 0.825, 0.922 and 0.961 at the coniferous catchment. The observed and simulated hyeto-hydrograph shoed that the time of lag to peak coincided well. Though the total runoff and peakflow of some events showed a discrepancy between the observed and simulated output, TOPMODEL could overall predict a hydrologic output at the estimation error less than 10 %. Therefore, TOPMODEL is useful tool for the prediction of runoff at an ungaged forested catchment in Korea.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.140-148
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• 2001

The satellite sensor model is typically established using ground control points acquired by ground survey Of existing topographic maps. In some cases where the targeted area can't be accessed and the topographic maps are not available, it is difficult to obtain ground control points so that geospatial information could not be obtained from satellite image. The paper presents several satellite sensor models and satellite image decomposition methods for non-accessible area where ground control points can hardly acquired in conventional ways. First, 10 different satellite sensor models, which were extended from collinearity condition equations, were developed and then the behavior of each sensor model was investigated. Secondly, satellite images were decomposed and also pseudo images were generated. The satellite sensor model extended from collinearity equations was represented by the six exterior orientation parameters in 1$^{st}$, 2$^{nd}$ and 3$^{rd}$ order function of satellite image row. Among them, the rotational angle parameters such as $\omega$(omega) and $\phi$(phi) correlated highly with positional parameters could be assigned to constant values. For non-accessible area, satellite images were decomposed, which means that two consecutive images were combined as one image. The combined image consists of one satellite image with ground control points and the other without ground control points. In addition, a pseudo image which is an imaginary image, was prepared from one satellite image with ground control points and the other without ground control points. In other words, the pseudo image is an arbitrary image bridging two consecutive images. For the experiments, SPOT satellite images exposed to the similar area in different pass were used. Conclusively, it was found that 10 different satellite sensor models and 5 different decomposed methods delivered different levels of accuracy. Among them, the satellite camera model with 1$^{st}$ order function of image row for positional orientation parameters and rotational angle parameter of kappa, and constant rotational angle parameter omega and phi provided the best 60m maximum error at check point with pseudo images arrangement.

• Journal of Korean Ophthalmic Optics Society
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• v.15 no.2
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• pp.145-150
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• 2010

Purpose: In this study we investigated the correlation between the effect of myopia correction and the change of corneal eccentricity using reverse geometry lens. Methods: The 23 students (46 eyes) continuously wearing reverse geometry lens during 3 months were divided into Group I and Group II by different parameter fitting methods of wearing Reverse Geometry Lens. We measured a corneal eccentricity for Group I and Group II at $10^{\circ}$, $20^{\circ}$, and $30^{\circ}$ positions from corneal apex before wearing reverse geometry lens, 1 week, 1 month, and 3 months after wearing reverse geometry lens. We also measured an uncorrected visual acuity, a spherical equivalent, and a corneal radius and analyzed the correlation between them and the change of corneal eccentricity using statistical significance test. Results: There were the statistical significances of a change of corneal eccentricity (p=0.03, t=-2.29) for Group I and Group II at 10 position from corneal apex in a week after wearing reverse geometry lens, but were not those (p>0.05) in 1 month, and 3 months after wearing reverse geometry lens. There were the statistical significances of correlation between the change of corneal eccentricity and a corrected visual acuity, and a corneal radius, respectively. Particularly, the high correlation between the change of corneal eccentricity and a corrected visual acuity (r=-0.36, p=0.00, t=6.5), and a spherical equivalent (r=-0.72, p=0.00, t=-70.5) for Group II in a week after wearing reverse geometry lens showed. Conclusions: We knew from these results that the high correlation between the effect of myopia correction and the change of corneal eccentricity in a week after wearing reverse geometry lens represented.

• Journal of Animal Environmental Science
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• v.18 no.3
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• pp.257-266
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• 2012

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereal and dired animal forages. Analysis of forage quality by NIRS usually involves dry grinding samples. Costs might be reduced if samples could be analyzed without drying or grinding. The objective of this study was to investigate effect of sample preparations on prediction ability of chemical composition and fermentation parameter for Italian ryegrass silages by NIRS. A population of 147 Italian ryegrass silages representing a wide range in chemical parameters were used in this investigation. Samples were scanned at 1nm intervals over the wavelength range 680-2500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in oven-dried grinding and fresh ungrinding condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with four spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV) and maximizing the correlation coefficient of cross validation (${R^2}_{CV}$). The results of this study show that NIRS predicted the chemical parameters with high degree of accuracy in oven-dried grinding treatment except for moisture contents. Prediction accuracy of the moisture contents was better for fresh ungrinding treatment (SECV 1.37%, $R^2$ 0.96) than for oven-dried grinding treatments (SECV 4.31%, $R^2$ 0.68). Although the statistical indexes for accuracy of the prediction were the lower in fresh ungrinding treatment, fresh treatment may be acceptable when processing is costly or when some changes in component due to the processing are expected. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation parameter of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.2
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• pp.61-71
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• 2009

Most deciduous trees in temperate zone are dormant during the winter to overcome cold and dry environment. Dormancy of deciduous fruit trees is usually separated into a period of rest by physiological conditions and a period of quiescence by unfavorable environmental conditions. Inconsistent and fewer budburst in pear orchards has been reported recently in South Korea and Japan and the insufficient chilling due to warmer winters is suspected to play a role. An accurate prediction of the flowering time under the climate change scenarios may be critical to the planning of adaptation strategy for the pear industry in the future. However, existing methods for the prediction of budburst depend on the spring temperature, neglecting potential effects of warmer winters on the rest release and subsequent budburst. We adapted a dormancy clock model which uses daily temperature data to calculate the thermal time for simulating winter phenology of deciduous trees and tested the feasibility of this model in predicting budburst and flowering of Niitaka pear, one of the favorite cultivars in Korea. In order to derive the model parameter values suitable for Niitaka, the mean time for the rest release was estimated by observing budburst of field collected twigs in a controlled environment. The thermal time (in chill-days) was calculated and accumulated by a predefined temperature range from fall harvest until the chilling requirement (maximum accumulated chill-days in a negative number) is met. The chilling requirement is then offset by anti-chill days (in positive numbers) until the accumulated chill-days become null, which is assumed to be the budburst date. Calculations were repeated with arbitrary threshold temperatures from $4^{\circ}C$ to $10^{\circ}C$ (at an interval of 0.1), and a set of threshold temperature and chilling requirement was selected when the estimated budburst date coincides with the field observation. A heating requirement (in accumulation of anti-chill days since budburst) for flowering was also determined from an experiment based on historical observations. The dormancy clock model optimized with the selected parameter values was used to predict flowering of Niitaka pear grown in Suwon for the recent 9 years. The predicted dates for full bloom were within the range of the observed dates with 1.9 days of root mean square error.

• Korean Journal of Remote Sensing
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• v.17 no.1
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• pp.33-44
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• 2001

The paper presents several satellite models and satellite image decomposition methods for inaccessible area where ground control points can hardly acquired in conventional ways. First, 10 different satellite sensor models, which were extended from collinearity condition equations, were developed and then behavior of each sensor model was investigated. Secondly, satellite images were decomposed and also pseudo images were generated. The satellite sensor model extended from collinearity equations was represented by the six exterior orientation parameters in $1^{st}$, $2^{nd}$ and $3^{rd}$ order function of satellite image row. Among them, the rotational angle parameters such as $\omega$(omega) and $\Phi$(phi) correlated highly with positional parameters could be assigned to constant values. For inaccessible area, satellite images were decomposed, which means that two consecutive images were combined as one image, The combined image consists of one satellite image with ground control points and the other without ground control points. In addition, a pseudo image which is an imaginary image, was prepared from one satellite image with ground control points and the other without ground control points. In other words, the pseudo image is an arbitrary image bridging two consecutive images. For the experiments, SPOT satellite images exposed to the similar area in different pass were used. Conclusively, it was found that 10 different satellite sensor models and 5 different decomposed methods delivered different levels of accuracy. Among them, the satellite camera model with 1st order function of image row for positional orientation parameters and rotational angle parameter of kappa, and constant rotational angle parameter omega and phi provided the best 60m maximum error at check point with pseudo images arrangement.