• Bulletin of the Korean Chemical Society
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• v.19 no.8
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• pp.868-874
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• 1998

An equivalent retention has been experimentally observed in thermal field-flow fractionation (ThFFF) for different polymer-solvent systems. It is shown that iso-retention between two sets of polymer-solvent systems can be obtained by adjusting the temperature difference (ΔT) according to the difference in the ratio of ordinary diffusion coefficient to thermal diffusion coefficient. This method uses a compensation of field strength (ΔT) in ThFFF at a fixed condition of cold wall temperature. It is applied for the calculation of molecular weight of polymers based on a calibration run of different standards obtained at an adjusted AT. The polymer standards used in this study are polystyrene (PS), polymethylmethacrylate (PMMA), and polytetrahydrofuran (PTHF). Three carrier solvents, tetrahydrofuran (THF), methylethylketone (MEK) and ethylacetate (ETAc) were employed. Though the accuracy in the calculation of molecular weight is dependent on the difference in the slope of log λ vs. log M which is related to Mark-Houwink constant a, it shows reasonable agreement within about 6% of relative error in molecular weight calculation for the polymer-solvent systems having similar a value.

• Asian-Australasian Journal of Animal Sciences
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• v.10 no.6
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• pp.614-620
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• 1997

Pedigree data were used to predict the performance of 1,166 imported Americas Brahman breeder cattle, composed of 104 bulls and 1,062 heifers with an average age of 24.4 months (range of 17 to 40 months). A total of 13 full sib, 10 maternal half sib, and 228 paternal half sib groups were identified, each with average group size of 2.08, 2.00, and 4.49, respectively. Only 758 (64.9% of total) imported cattle were found to have at least one ancestor with expected progeny differences (EPDs) reported in the Spring 1995 Brahman Sire Summary. Moderate average accuracy values of .71, .69, .52, and .52 for birth weight, weaning weight, yearling weight, and maternal milk, respectively, were noted for EPDs of the ancestors. Prediction equations were derived by multiple regression analysis of available EPDs of sire, paternal grand sire, and maternal grand sire. Based on pedigree indexes that involve various combinations of available ancestral information, the average predicted EPDs (lbs) for imported cattle were $1.76{\pm}0.54$, $14.93{\pm}4.86$, $25.10{\pm}9.50$, and $5.86{\pm}2.08$ for birth weight, weaning weight, yearling weight, and maternal milk, respectively. Significant correlations (p < .05) were also found between sire and son EPDs (+.27) for yearling weight; and between sire and paternal grand sire EPDs for birth weight (+.34), weaning weight (+.51), yearling weight (+.49), and maternal milk (+.55).

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.395-409
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• 2022

The purpose of this study is to evaluate the classification performance and applicability when land cover datasets constructed for AI training are cross validation to other areas. For study areas, Gyeongsang-do and Jeolla-do in South Korea were selected as cross validation areas, and training datasets were obtained from AI-Hub. The obtained datasets were applied to the U-Net algorithm, a semantic segmentation algorithm, for each region, and the accuracy was evaluated by applying them to the same and other test areas. There was a difference of about 13-15% in overall classification accuracy between the same and other areas. For rice field, fields and buildings, higher accuracy was shown in the Jeolla-do test areas. For roads, higher accuracy was shown in the Gyeongsang-do test areas. In terms of the difference in accuracy by weight, the result of applying the weights of Gyeongsang-do showed high accuracy for forests, while that of applying the weights of Jeolla-do showed high accuracy for dry fields. The result of land cover classification, it was found that there is a difference in classification performance of existing datasets depending on area. When constructing land cover map for AI training, it is expected that higher quality datasets can be constructed by reflecting the characteristics of various areas. This study is highly scalable from two perspectives. First, it is to apply satellite images to AI study and to the field of land cover. Second, it is expanded based on satellite images and it is possible to use a large scale area and difficult to access.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.3
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• pp.245-258
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• 2012

ILM(incremental launching method) is a way of construction, installing a girder producing spot behind the abutment, making the bridge girder infilled with concrete continuously and launching with using by jack. The superstructure of the bridge constructed by this method is temporarily located on the center of the span and the supporting points under construction. Therefore, the sections are structurally undergone maximum positive moment, maximum negative moment, and maximum shear force arising from self weight. On the other hand, launching nose is attached to the front of the girder to decrease the cantilever effect. The magnitude of this temporary stress creating on the upper section is dependent upon the launching nose's characteristics. This study has proposed an analysis formula simplified on the assumption that the launching nose section is a quasi-equivalent section(rigid; equivalent section, weight; tapered section) in order to ensure the accuracy of the analysis formula and improve its usage with reference to the interaction between the launching nose and the upper section; and a prismatic analysis formula modified by displacing a diaphragm's weight by a concentrated load in order to improve the accuracy of the existing analysis formula that assumes the launching nose section as the equivalent section. To judge the accuracy and usage of two analysis formulas proposed, we have compared and analyzed computational structural analysis programs and existing analysis formulas based on actual ILM bridge data. As a result, all of two reveal the superior accuracy and also their usage has been improved by the simplification of analysis formulas.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.6
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• pp.765-772
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• 2017

Objective: This study examines the genetic factors influencing the phenotypes (four economic traits:oleic acid [C18:1], monounsaturated fatty acids, carcass weight, and marbling score) of Hanwoo. Methods: To enhance the accuracy of the genetic analysis, the study proposes a new statistical model that excludes environmental factors. A statistically adjusted, analysis of covariance model of environmental and genetic factors was developed, and estimated environmental effects (covariate effects of age and effects of calving farms) were excluded from the model. Results: The accuracy was compared before and after adjustment. The accuracy of the best single nucleotide polymorphism (SNP) in C18:1 increased from 60.16% to 74.26%, and that of the two-factor interaction increased from 58.69% to 87.19%. Also, superior SNPs and SNP interactions were identified using the multifactor dimensionality reduction method in Table 1 to 4. Finally, high- and low-risk genotypes were compared based on their mean scores for each trait. Conclusion: The proposed method significantly improved the analysis accuracy and identified superior gene-gene interactions and genotypes for each of the four economic traits of Hanwoo.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.81-86
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• 2016

Term weighting is a popular technique that effectively weighs the term features to improve accuracy in document classification. While several successful term weighting algorithms have been suggested, none of them appears to perform well consistently across different data domains. In this paper we propose several reasonable methods to combine different term weight vectors to yield a robust document classifier that performs consistently well on diverse datasets. Specifically we suggest two approaches: i) learning a single weight vector that lies in a convex hull of the base vectors while minimizing the class prediction loss, and ii) a mini-max classifier that aims for robustness of the individual weight vectors by minimizing the loss of the worst-performing strategy among the base vectors. We provide efficient solution methods for these optimization problems. The effectiveness and robustness of the proposed approaches are demonstrated on several benchmark document datasets, significantly outperforming the existing term weighting methods.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.3
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• pp.76-82
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• 2021

At present, with the development of precision instruments, high dimensional accuracy of workpieces must be ensured. In particular, for the aluminum alloys used in automobiles, the surface roughness of the workpiece is extremely important. The dimensional accuracy and surface roughness of the workpiece is considerably affected by the rotational accuracy of the rotor. Therefore, to enhance the rotational accuracy, various variables such as those related to the components such as bearings, motors, and end mills, rotational speeds, and vibrations must be considered. In this study, the difference in the quality of the workpieces was compared considering the weight imbalance and rotational speed as variables.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.441-449
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• 2018

Deep learning has been used for various processing centered on image recognition. One core algorithms of the deep learning, convolutional neural network is an deep neural network that specialized in image recognition. In this paper, we use a convolutional neural network to classify forest insects and propose an optimization method. Experiments were carried out by combining two weight initialization and six parameter update methods. As a result, the Xavier-SGD method showed the highest performance with an accuracy of 82.53% in the 12 different combinations of experiments. Through this, the latest learning algorithms, which complement the disadvantages of the previous parameter update method, we conclude that it can not lead to higher performance than existing methods in all application environments.

• Journal of Information Processing Systems
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• v.6 no.1
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• pp.79-90
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• 2010

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. In many application areas, mining frequent itemsets has been suggested to find important frequent itemsets by considering the weight of itemsets. In this paper, we present an efficient algorithm WSFI (Weighted Support Frequent Itemsets)-Mine with normalized weight over data streams. Moreover, we propose a novel tree structure, called the Weighted Support FP-Tree (WSFP-Tree), that stores compressed crucial information about frequent itemsets. Empirical results show that our algorithm outperforms comparative algorithms under the windowed streaming model.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.4
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• pp.229-237
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• 2022

Motion recognition is very useful for implementing an intuitive HMI (Human-Machine Interface). In particular, hands are the body parts that can move most precisely with relatively small portion of energy. Thus hand motion has been used as an efficient communication interface with other persons or machines. In this paper, we design and implement a light-weight ANN (Artificial Neural Network)-based hand motion recognition using a state-of-the-art flex sensor. The proposed design consists of data collection from a wearable flex sensor, preprocessing filters, and a light-weight NN (Neural Network) classifier. For verifying the performance and functionality of the proposed design, we implement it on a low-end embedded device. Finally, our experiments and prototype implementation demonstrate that the accuracy of the proposed hand motion recognition achieves up to 98.7%.