• Asian-Australasian Journal of Animal Sciences
• /
• v.17 no.4
• /
• pp.523-532
• /
• 2004

An experiment was conducted to investigate the effect of microbial phytase ($Natuphos^{R}$) supplementation in combination with enzyme complex (composed of enzymes targeted to SBM dietary components such as $\alpha$-galactosides and galactomannans; $Endo-Power^{R}$) to diet with low nutrient levels on growth performance and ileal nutrient digestibility of weaned pigs. A total of 210 crossbred weaned pigs (Landrace$\times$Yorkshire$\times$Duroc), 6.68$\pm$0.98 kg of initial body weight, were randomly allotted to five dietary treatments, based on weight and age, according to a randomized complete block design. There were three pens per treatment and 14 pigs per pen. The dietary treatments were 1) CON (Control diet with no phytase and enzyme complex (EC)), 2) LP+EC 100 (Control diet with 0.15% unit lower available phosphorus (aP) level+0.1% phytase (500 FTU/kg diet) and 0.1% enzyme complex), 3) LP+EC 80 (Control diet with 0.15% unit lower aP level+0.08% phytase (400 FTU/kg diet) and 0.08% enzyme complex, 4) LPEA+EC 100 (Control diet with 0.15% unit lower aP and 3% lower ME and amino acid levels (lysine, methionine, threonine and typtophan)+0.1% phytase (500 FTU/kg diet) and 0.1% enzyme complex), 5) LPEA+EC 80 (Control diet with 0.15% unit lower aP and 3% lower ME and amino acid levels+0.08% phytase (400 FTU/ kg diet) and 0.08% enzyme complex). For the determination of ileal nutrients digestibility, a total of 15 T-cannulated pigs (initial body weight; 7.52$\pm$1.24 kg; 3 replicates per treatment) were used in the present study. Piglets were weighted and allotted into same dietary treatments as one in growth trial and phase I experimental diets were provided for ileal digestibility study. There was no significant difference (p>0.05) in average daily gain (ADG) and average daily feed intake (ADFI) among dietary treatments during the whole experimental period (0 to 5 weeks). However, piglets in LP+EC 100 group had a significantly higher gain/feed ratio (G:F) than piglets had in control (p<0.05). Crude protein, energy and phosphorus digestibilities were significantly improved when both of phytase and enzyme complex were supplemented at the revel of 0.1%, respectively to diets with low nutrient level (aP or (and) ME and amino acids) (p<0.05). Piglets in LP+EC 100 and LPEA+EC 100 groups showed significantly higher phosphorus content (%) in bone than that of piglets in control group (p<0.05). Supplementation of both of phytase and enzyme complex at 0.1%, respectively, to diet with low nutrient levels (aP or (and) ME and amino acids) significantly improved total ileal essential amino acid and nonessential amino acid digestibilities compared to control group (p<0.05). In conclusion, the results from the present study suggest that the simultaneous inclusion of phytase and enzyme complex to diets at recommended level is advantageous with respect to improving growth performance and nutrient digestibility of weaned pigs and may contribute to increased economic return when added to corn-soy based weaned pig diets.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.4
• /
• pp.108-115
• /
• 2009

In this paper, we propose a novel true-motion estimation technique supporting efficient frame error concealment for error-resilient video coding. In general, it is important to accurately obtain the true-motion of objects in video sequences for effectively recovering the corrupted frame due to transmission errors. However, the conventional motion estimation (ME) technique, which minimizes a sum of absolute different (SAD) between pixels of the current block and the motion-compensated block, does not always reflect the true-movement of objects. To solve this problem, we introduce a new metric called an absolute difference of motion vectors (ADMV) which is the distance between motion vectors of the current block and its motion-compensated block. The proposed ME method can prevent unreliable motion vectors by minimizing the weighted combination of SAD and ADMV. In addition, the proposed ME method can significantly improve the performance of error concealment at the decoder since error concealment using the ADMV can effectively recover the missing motion vector without any information of the lost frame. Experimental results show that the proposed method provides similar coding efficiency to the conventional ME method and outperforms the existing error-resilient method.

• Journal of Biomedical Engineering Research
• /
• v.19 no.2
• /
• pp.119-128
• /
• 1998

This paper describes a filtering algorithm which employs apriori information of SPECT lesion detectability potential for the filtering of degraded projection images prior to the backprojection reconstruction. In this algorithm, we determined m minimum detectable lesion sized(MDLSs) by assuming m object contrasts uniformly-chosen in the range of 0.0-1.0, based on a signal/noise model which provides the capability potential of SPECT in terms of physical factors. A best estimate of given projection image is attempted as a weighted combination of the subimages from m optimal filters whose design is focused on maximizing the local S/N ratios for the MDLS-lesions. These subimages show relatively larger resolution recovery effect and relatively smaller noise reduction effect with the decreased MDLS, and the weighting on each subimage was controlled by the difference between the subimage and the maximum-resolution-recovered projection image. The proposed filtering algoritym was tested on SPECT image reconstruction problems, and produced good results. Especially, this algorithm showed the adaptive effect that approximately averages the filter outputs in homogeneous areas and sensitively depends on each filter strength on contrast preserving/enhancing in textured lesion areas of the reconstructed image.

• Journal of Korean Society for Geospatial Information Science
• /
• v.24 no.1
• /
• pp.89-98
• /
• 2016

In this paper, we proposed an automated areal feature matching method based on geometric similarity without user intervention and is applied into areal features of many-to-many relation, for confusion of spatial data-sets of different scale and updating cycle. Firstly, areal feature(node) that a value of inclusion function is more than 0.4 was connected as an edge in adjacency matrix and candidate corresponding areal features included many-to-many relation was identified by multiplication of adjacency matrix. For geometrical matching, these multiple candidates corresponding areal features were transformed into an aggregated polygon as a convex hull generated by a curve-fitting algorithm. Secondly, we defined matching criteria to measure geometrical quality, and these criteria were changed into normalized values, similarity, by similarity function. Next, shape similarity is defined as a weighted linear combination of these similarities and weights which are calculated by Criteria Importance Through Intercriteria Correlation(CRITIC) method. Finally, in training data, we identified Equal Error Rate(EER) which is trade-off value in a plot of precision versus recall for all threshold values(PR curve) as a threshold and decided if these candidate pairs are corresponding pairs or not. To the result of applying the proposed method in a digital topographic map and a base map of address system(KAIS), we confirmed that some many-to-many areal features were mis-detected in visual evaluation and precision, recall and F-Measure was highly 0.951, 0.906, 0.928, respectively in statistical evaluation. These means that accuracy of the automated matching between different spatial data-sets by the proposed method is highly. However, we should do a research on an inclusion function and a detail matching criterion to exactly quantify many-to-many areal features in future.

• Journal of Korea Water Resources Association
• /
• v.55 no.8
• /
• pp.589-601
• /
• 2022

Although drought is a natural phenomenon, its damage occurs in combination with regional physical and social factors. Especially, related to the supply and demand of various waters, drought causes great socio-economic damage. Even meteorological droughts occur with similar severity, its impact varies depending on the regional characteristics and water supply system. Therefore, this study assessed regional drought risk considering regional socio-economic factors and water supply system. Drought hazard was assessed by grading the joint drought management index (JDMI) which represents water shortage. Drought vulnerability was assessed by weighted averaging 10 socio-economic factors using Entropy, Principal Component Analysis (PCA), and Gaussian Mixture Model (GMM). Drought response capacity that represents regional water supply factors was assessed by employing Bayesian networks. Drought risk was determined by multiplying a cubic root of the hazard, vulnerability, and response capacity. For the drought hazard meaning the possibility of failure to supply water, Goesan-gun was the highest at 0.81. For the drought vulnerability, Daejeon was most vulnerable at 0.61. Considering the regional water supply system, Sejong had the lowest drought response capacity. Finally, the drought risk was the highest in Cheongju-si. This study identified the regional drought risk and vulnerable causes of drought, which is useful in preparing drought mitigation policy considering the regional characteristics in the future.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.17 no.2
• /
• pp.45-58
• /
• 2012

For the development of reference values and evaluation of water quality in various environmental conditions, we divided the coastal region around Korean peninsular into 5 distinctive ecological regions based on the influence of surface current, depth, tidal range, turbidity, and climate condition. We used national marine environment monitoring data collected by National Fisheries Research & Development Institute(NFRDI) from 2000-2009. For the reference values, we used maximum seasonal mean from 2000 to 2007 for DIN, DIP, and chlorophyll-a and minimum seasonal mean for secchi depth measured at stations without the influence of river runoff in each ecological regions. For the reference value of bottom dissolved oxygen saturation, we used minimum mean value of 90% calculated from minimal riverine influence stations of whole regions. We calculated enrichment score for each assessment criteria. The enrichment score of DIN, DIP, and Chlorophyll-a was 1 (=< reference value), 2 (< 110% of reference value), 3 (< 125% of reference value), 4 (< 150% of reference value), and 5 (> 150% of reference value). The enrichment score of DO saturation and Secchi depth was 1 (> reference value), 2 (> 90% of reference value), 3 (>75 % of reference value), 4 (> 50% of reference value), and 5 (< 50% of reference value). We calculated water quality index using weighted linear combination of five enrichment score for the comparison of whole regions. From the water quality index distribution calculated from all stations between 2000 and 2007 period, we classified into 5 grade based on the standard deviation calculated from total water quality index. We assigned grade very good(I), good(II), moderate(III), bad(IV), and very bad(V) when the water quality index was less than 23, minimum + 1 sd, +2 sd, +3 sd, and grater than minium+ 3 sd, respectively.

• Radiation Oncology Journal
• /
• v.15 no.1
• /
• pp.71-78
• /
• 1997

Purpose : To collect beam data for dynamic wedge fields using conventional measurement tools without the multi-detector system, such as the linear diode detectors or ionization chambers. Materials and Methods : The accelerator CL 2100 C/D has two photon energies of 6MV and 15MV with dynamic wedge an91es of 15o, 30o, 45o and 60o. Wedge transmission factors, percentage depth doses(PDD's) and dose Profiles were measured. The measurements for wedge transmission factors are performed for field sizes ranging from $4\times4cm^2\;to\;20\times20cm^2$ in 1-2cm steps. Various rectangular field sizes are also measured for each photon energy of 6MV and 15MV, with the combination of each dynamic wedge angle of 15o 30o. 45o and 60o. These factors are compared to the calculated wedge factors using STT(Segmented Treatment Table) value. PDD's are measured with the film and the chamber in water Phantom for fixed square field. Converting parameters for film data to chamber data could be obtained from this procedure. The PDD's for dynamic wedged fields could be obtained from film dosimetry by using the converting parameters without using ionization chamber. Dose profiles are obtained from interpolation and STT weighted superposition of data through selected asymmetric static field measurement using ionization chamber. Results : The measured values of wedge transmission factors show good agreement to the calculated values The wedge factors of rectangular fields for constant V-field were equal to those of square fields The differences between open fields' PDDs and those from dynamic fields are insignificant. Dose profiles from superposition method showed acceptable range of accuracy(maximum 2% error) when we compare to those from film dosimetry. Conclusion : The results from this superposition method showed that commissionning of dynamic wedge could be done with conventional dosimetric tools such as Point detector system and film dosimetry winthin maximum 2% error range of accuracy.

• Journal of Practical Agriculture & Fisheries Research
• /
• v.23 no.1
• /
• pp.89-104
• /
• 2021

Based on the TF-IDF weighted value that evaluates the importance of words that play a key role, the semantic network analysis(SNA) was conducted on the self-introduction letter of freshman at Korea National College of Agriculture and Fisheries(KNCAF) in 2020. The top three words calculated by TF-IDF weights were agriculture, mathematics, study (Q. 1), clubs, plants, friends (Q. 2), friends, clubs, opinions, (Q. 3), mushrooms, insects, and fathers (Q. 4). In the relationship between words, the words with high betweenness centrality are reason, high school, attending (Q. 1), garbage, high school, school (Q. 2), importance, misunderstanding, completion (Q.3), processing, feed, and farmhouse (Q. 4). The words with high degree centrality are high school, inquiry, grades (Q. 1), garbage, cleanup, class time (Q. 2), opinion, meetings, volunteer activities (Q.3), processing, space, and practice (Q. 4). The combination of words with high frequency of simultaneous appearances, that is, high correlation, appeared as 'certification - acquisition', 'problem - solution', 'science - life', and 'misunderstanding - concession'. In cluster analysis, the number of clusters obtained by the height of cluster dendrogram was 2(Q.1), 4(Q.2, 4) and 5(Q. 3). At this time, the cohesion in Cluster was high and the heterogeneity between Clusters was clearly shown.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.139-156
• /
• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.