• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.10 no.1
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• pp.7-14
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• 2004

Migration behavior of selected solvents and food samples showed differences of the chemical structures and polarities, the food samples which have similar polar expresses more higher affinity than different polar degrees. Water which is polar has a highest partitioning coefficient values on polar isopropanol, and oil which is nonpolar has highest partitioning value on non-polar toluene. The increasing order of partitioning values was accord with increasing water contents in food samples. It is showed that the wheat flour with 13.2% moisture content has the highest partitioning coefficient values on the isopropanol with -OH. Kp value of sugar showed remarkable lower partitioning coefficient values than other food samples due to high degree crystallinity. This phenomenon can be predictable with ${\delta}$ values, because order of partitioning coefficient values which comes out through the experiment and the sequence of Hildebrand solubility parameter value difference between food sample and printing ink solvent correspond almost. This Hildebrand solubility parameter value can be easily applied to the food package industry because the effect of food-safety can be considered without passing through complicated steps by using this method.

• Preventive Nutrition and Food Science
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• v.16 no.3
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• pp.267-271
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• 2011

Static Headspace Gas Chromatographic analysis was used to study the partitioning behavior of five organic printing ink solvents between chocolate cookie/air systems. Three cookie sample formulations varied with respect to chocolate type and overall percentage of constituents. Major considerations involved differences in fat content and type and resulting variability in chemical and physical structure. Each of the solvents studied (ethyl acetate, hexane, isopropanol, methyl ethyl ketone, toluene) represents a general class of printing ink solvents based on predominate functional group. Values of the partitioning coefficient (Kp) were determined at equilibrium using measured quantities of both solvent and cookie sample in closed systems at temperature of 25, 35, and $45^{\circ}C$. In each of the three cookies at the three test temperatures, toluene always exhibited the greatest value of partitioning to cookie and hexane always exhibited the least. Results also showed that the partitioning behavior of solvents is generally inversely related to temperature and that solvent affinity, though constant for a particular cookie type over all test temperatures, varies significantly among the three cookie types. The preference of each of the five solvents for each cookie sample was also found to vary with temperature. No correlation was found between the extent of partitioning and cookie formulation or physical characteristic of solvent. The Hildebrand parameter, related to ${\Delta}Hmix$ (heat of mixing), may be used to describe differences in partitioning based on the overall potential of a solvent/cookie interaction to occur. The potential for interaction is dependent upon the chemical structure of the cookie sample and thus the availability of 'active-sites' required for a given solvent.

• Preventive Nutrition and Food Science
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• v.14 no.4
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• pp.310-315
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• 2009

The partitioning behavior of the five printing ink solvents in nine lab-made cookies with various sugar and water content at 25${^{\circ}C}$ was studied to find out the presence and effects of interaction between the two ingredients on partitioning behavior in cookies. Solvents were ethyl acetate, hexane, isopropanol, methyl ethyl ketone and hexane. It was observed that the partition coefficient (the solvent concentration in food compared to that in air, Kp) decreased as sugar increased in all case and increased as water content increased for all compounds except toluene. Statistical analysis by the F-test method was used to determine the significance of sugar-water interactions, as well as other single factors on partitioning behavior of each solvent. Sugar content alone had no significant effects, but the crystallinity of sugar, as changed by water content, affected the partitioning behavior of the five solvents significantly. Parameter estimation for each significant factor by SAS program yielded a regression equation, which was used to predict the partitioning behavior in the finished cookie. Kp values from the regression equation could be determined more precisely by applying a correction term for the interaction between sugar and water to the Kp values of each ingredient after baking.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.316-325
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• 2003

A user preference prediction method using an exiting collaborative filtering technique has used the nearest-neighborhood method based on the user preference about items and has sought the user's similarity from the Pearson correlation coefficient. Therefore, it does not reflect any contents about items and also solve the problem of the sparsity. This study suggests the preference prediction system using the similarity weight granted Bayesian estimated value and the associative user clustering to complement problems of an exiting collaborative preference prediction method. This method suggested in this paper groups the user according to the Genre by using Association Rule Hypergraph Partitioning Algorithm and the new user is classified into one of these Genres by Naive Bayes classifier to slove the problem of sparsity in the collaborative filtering system. Besides, for get the similarity between users belonged to the classified genre and new users, this study allows the different estimated value to item which user vote through Naive Bayes learning. If the preference with estimated value is applied to the exiting Pearson correlation coefficient, it is able to promote the precision of the prediction by reducing the error of the prediction because of missing value. To estimate the performance of suggested method, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.11 no.3
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• pp.253-257
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• 2013

Geology, hydrogeology, and geochemistry are the main technical siting factors of a geological repository for spent nuclear fuels. This paper evaluated the siting criteria of overseas geological repository with related to hydrogeologic properties, such as hydraulic conductivity, partitioning coefficient, dispersion coefficient, boundary condition, and water age. Each country establishes the siting criteria based on its important geological backgrounds and information, and social environment. For example, Sweden and Finland that have decided a crystalline rock as a host rock of a geological repository show different siting criteria for hydraulic conductivity. In Sweden, it is preferable to avoid area where the hydraulic conductivity on a deposition hole scale (~30m) exceeds $10^{-8}m/s$, whereas Finland does not decide any criterion for the hydraulic conductivity because of limited data for it. In addition, partitioning coefficients should be less than 10-1 of average value in Swedish crystalline bedrock. However, the area where shows 100 times less than average partitioning coefficients of radionuclides in crystalline rock should be avoided in Sweden. In German, the partitioning coefficients for the majority of the long-term-relevant radionuclides should be greater than or equal to $0.001m^3/kg$. Therefore, it is strongly required to collect much and exact information for the hydrogeologic properties in order to set up the siting criteria.

• Korean Journal of Optics and Photonics
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• v.7 no.3
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• pp.207-212
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• 1996

We propose an extended additional layer method (EALM) of calculating the coupling coefficient of arbitrary shaped diffraction gratings. In EALM, to determine the unperturbed field distribtution, a grating region is replaced by a new uniform layer whose dielectric constant is the average value of the dielectric constant of a grating region in both longitudinal and transverse directions. Using this method, we calculate the coupling coefficient for a five-layer distributed feedback structure device with trapezoidal and triangular gratings. The validity of this method is established by comparing the results calculated by partitioning the grating region up to five uniform layers.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.9
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• pp.87-92
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• 1998

TM mode coupling coefficients for a generic five-layer DFB structure with trapezoidal and triangular gratings are calculated using the extended additional layer method. To determine the unperturbed field distributions of TM modes, a grating region is replaced by a new uniform layer whose inverse dielectric constant is the average value of the inverse dielectric constant of grating region in both longitudinal and transverse directions. Based on the self-consistent check, the validity of this method is established by comparing the results calculated by partitioning the grating region up to six uniform layers.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.9
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• pp.913-917
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• 2010

To inspect the defects of printed markings on the surface of IC package, the OCV (Optical Character Verification) method based on NCC (Normalized Correlation Coefficient) pattern matching is widely used. In order to detect the micro pattern defects appearing on the small portion of the markings, a Partitioned NCC pattern matching method was proposed to overcome the limitation of the NCC pattern matching. In this method, the reference pattern is first partitioned into several blocks and the NCC values are computed and are combined in these small partitioned blocks, rather than just using the NCC value for the whole reference pattern. In this paper, we proposed a method to decide the proper number of partition blocks and a method to inspect and combine the NCC values of each partitioned block to identify the defective markings.

• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.471-482
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• 2016

This paper presents an extraction method for water body which uses block-based image partitioning and extension of water body boundaries to improve the performance of supervised classification for water body extraction. The Mahalanobis distance image is created by computing the spectral information of Normalized Difference Water Index (NDWI) and Near Infrared (NIR) band images over a training site within the water body in order to extract an initial water body area. To reduce the effect of noise contained in the Mahalanobis distance image, we apply mean curvature diffusion to the image, which controls diffusion coefficients based on connectivity strength between adjacent pixels and then extract the initial water body area. After partitioning the extracted water body image into the non-overlapping blocks of same size, we update the water body area using the information of water body belonging to water body boundaries. The update is performed repeatedly under the condition that the statistical distance between water body area belonging to water body boundaries and the training site is not greater than a threshold value. The accuracy assessment of the proposed algorithm was tested using KOMPSAT-2 images for the various block sizes between $11{\times}11$ and $19{\times}19$. The overall accuracy and Kappa coefficient of the algorithm varied from 99.47% to 99.53% and from 95.07% to 95.80%, respectively.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.287-296
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• 2003

There has been much research focused on collaborative filtering technique in Recommender System. However, these studies have shown the First-Rater Problem and the Sparsity Problem. The main purpose of this Paper is to solve these Problems. In this Paper, we suggest the user's predicting preference method using Bayesian estimated value and the associative user clustering for the recalculation of preference. In addition to this method, to complement a shortcoming, which doesn't regard the attribution of item, we use Representative Attribute-Neighborhood method that is used for the prediction when we find the similar neighborhood through extracting the representative attribution, which most affect the preference. We improved the efficiency by using the associative user's clustering analysis in order to calculate the preference of specific item within the cluster item vector to the collaborative filtering algorithm. Besides, for the problem of the Sparsity and First-Rater, through using Association Rule Hypergraph Partitioning algorithm associative users are clustered according to the genre. New users are classified into one of these genres by Naive Bayes classifier. In addition, in order to get the similarity value between users belonged to the classified genre and new users, and this paper allows the different estimated value to item which user evaluated through Naive Bayes learning. As applying the preference granted the estimated value to Pearson correlation coefficient, it can make the higher accuracy because the errors that cause the missing value come less. We evaluate our method on a large collaborative filtering database of user rating and it significantly outperforms previous proposed method.