• Journal of Embryo Transfer
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• v.27 no.1
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• pp.37-43
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• 2012

This study evaluated a method of sorting X and Y chromosomes based on size using the forward angle light scatter related refractive index (FSC) of a flow cytometer. Hanwoo bulls sperm were separated to X and Y chromosomes by the parameters of FSC or Hoechst 33342 intensity. As a result, using monitor program linked flow cytometry during sorting processing, the purities were $97{\pm}0.57$ or $96{\pm}0.67%$ for the X-fraction and $96{\pm}0.33$ or $97{\pm}1.33%$ for the Y-fraction in the two sperm sorting methods. There were no differences in the X and Y ratios (X and Y %) between the sperm sorting methods based on FSC or DNA content. The proportions of female and male embryos used for in vitro fertilization and development were $66.03{\pm}3.31$ or $69.37{\pm}1.41%$, and $70.56{\pm}2.42$ or $56.11{\pm}3.09%$ when sperm were processed using the sex sorting method by FSC or Hoechst 33342. In conclusion, further study is needed to determine the optimum procedure and improve the nozzle to enhancing sorting accuracy or efficiency. Also, the findings of this study do not negate the possibility that the difference method of sperm sorting cannot use a UV laser beam.

• Journal of the Korean Society for Library and Information Science
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• v.39 no.2
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• pp.123-146
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• 2005

This study aims to find consistent strategies for feature selection and feature weighting methods, which can improve the effectiveness and efficiency of kNN text classifier. Feature selection criteria and feature weighting methods are as important factor as classification algorithms to achieve good performance of text categorization systems. Most of the former studies chose conflicting strategies for feature selection criteria and weighting methods. In this study, the performance of several feature selection criteria are measured considering the storage space for inverted index records and the classification time. The classification experiments in this study are conducted to examine the performance of IDF as feature selection criteria and the performance of conventional feature selection criteria, e.g. mutual information, as feature weighting methods. The results of these experiments suggest that using those measures which prefer low-frequency features as feature selection criterion and also as feature weighting method. we can increase the classification speed up to three or five times without loosing classification accuracy.

• Korean Journal of Construction Engineering and Management
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• v.8 no.6
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• pp.246-262
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• 2007

The performance of projects has always been an area of interest in the construction industry. Roles of all construction supply chain partners are necessary; however the role of a contractor firm in the construction project is pivotal. So, this research intended to explore a Construction Firm's performance criteria which could measure the level of performance of that firm in an ongoing project. Data was collected from construction professionals working in three principal project participant organizations, namely Owner, Consultant and Contractor. A total of 113 nos. of performance measuring items were sorted from literature review and used to collect data. Statistical tools processed by SPSS program was employed to analyze the data. Out of total 113 items, only 65 nos. of variables were found to be acceptable to every population group of this study. Factor analysis revealed 12 key performance predicting factors (KPPF) with 53 predictive indicators. 12 KPPFS with index weight are: work progress and smoothening (9.3%), change order management and work accuracy (9.1%), business relationship building (8.1%), adequacy of construction work procedure (8.6%), quality performance (8.0%), health and site safety adequacy (8.8%), Innovative contractor (8.0%), adequacy of construction site information (6.8%), compliance with contract plan/specification requirements (8.9%), creditworthiness and financial capability (8.3%), intra-agency relationship and responsiveness (7.0%) and resource management (9.2%). These results could be useful to project management body to evaluate performance of its contractor firm on site as well as the contractor itself to assess own performance and its subcontractors on-site.

• Journal of The Korean Astronomical Society
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• v.45 no.2
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• pp.31-38
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• 2012

In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.226-231
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• 2019

A WTCI is an important criteria for evaluating an mount of patient's tongue coating in tongue diagnosis. However, Previous WTCI tongue coating evaluation methods is a most of quantitatively measuring ration of the extracted tongue coating region and tongue body region, which has a non-objective measurement problem occurring by exposure conditions of tongue image or the recognition performance of tongue coating. Therefore, a WTCI based on deep learning is proposed for classifying an amount of tonger coating in this paper. This is applying the AI deep learning method using big data. to WTCI for evaluating an amount of tonger coating. In order to verify the effectiveness performance of the deep learning in tongue coating evaluating method, we classify the 3 types class(no coating, some coating, intense coating) of an amount of tongue coating by using CNN model. As a results by testing a building the tongue coating sample images for learning and verification of CNN model, proposed method is showed 96.7% with respect to the accuracy of classifying an amount of tongue coating.

• Korean Journal of Agricultural Science
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• v.39 no.2
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• pp.227-242
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• 2012

Accurate estimation of milk production cost is very important for dairy farmers in establishing strategies for business management (e.g. planning a program for milk production, deciding the size of business and investment, determining the milk price for sale). Since the estimated cost of milk production is used as an important index to determine the basal price of milk in Korea, there has been much interest and debate on the method used to estimate milk production cost among the stakeholder. This study was thus carried out to identify problems in the current methodology for estimating cost of milk production, and to find a better way to improve it. We propose several alternatives and better ways to improve the current method for estimating cost of milk production. Estimation of the income and cost per head should be based on the number of cattle converted to grown cows. Cost estimation per liter of milk should be made for both whole milk and 3.4% milk fat corrected milk. The value of purchased cows and raised replacement heifers should be the same as their market value. The productive life span of cows should be less 4 years, and the terminal or salvage value of cows needs to be 30 to 40% less than her initial value. When calculating depreciation of cows over the productive life span, however, the salvage value should be 0 or 1 Korean won. On calculating labor costs, the farm labor wage corresponding to the average wage of nonfarm industrial workers should be assumed. Beside of these, better estimation procedures for other items are also given. The proposed methods from this study should improve the accuracy of estimation of milk production cost and help to achieve consensus among the stakeholder.

• Safety and Health at Work
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• v.5 no.2
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• pp.80-85
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• 2014

Background: Currently available questionnaires for evaluating the quality of worklife do not fully examine every factor related to worklife in all cultures. A tool in Thai is therefore needed for the direct evaluation of the quality of worklife. Our aim was to translate the Work-related Quality of Life Scale-2 (WRQLS-2) into Thai, to assess the validity and reliability of the Thai-translated version, and to examine the tool's accuracy vis-$\grave{a}$-vis nursing in Thailand. Methods: This was a descriptive correlation study. Forward and backward translations were performed to develop a Thai version of the WRQLS. Six nursing experts participated in assessing content validity and 374 registered nurses (RNs) participated in its testing. After a 2-week interval, 67 RNs were retested. Structural validity was examined using principal components analysis. The Cronbach's alpha values were calculated. The respective independent sample t test and intraclass correlation coefficient were used to analyze known-group validity and test-retest reliability. Multistate sampling was used to select 374 RNs from the In- and Outpatient Department of Srinagarind Hospital of the Khon Kaen University (Khon Kaen, Thailand). Results: The content validity index of the scale was 0.97. Principal components analysis resulted in a seven-factor model, which explains 59% of the total variance. The overall Cronbach's alpha value was 0.925, whereas the subscales ranged between 0.67 and 0.82. In the assessment results, the known-group validity was established for the difference between civil servants and university employees [F (7.982, 0.005) and t (3.351; p < 0.05)]. Civil servants apparently had a better quality worklife, compared to university employees. Good test-retest reliability was observed (r = 0.892, p < 0.05). Conclusion: The Thai version of a WRQLS appears to be well validated and practicable for determining the quality of the work-life among nurses in Thailand.

• Atmosphere
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• v.27 no.2
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• pp.189-197
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• 2017

Progressing from weather forecasts and warnings to multi-hazard impact-based forecast and warning services represents a paradigm shift in service delivery. Urban flooding is a typical meteorological disaster. This study proposes support plan for urban flooding impact-based forecast by providing inundation risk matrix. To achieve this goal, we first configured storm sewer management model (SWMM) to analyze 1D pipe networks and then grid based inundation analysis model (GIAM) to analyze 2D inundation depth over the Gangnam drainage area with $7.4km^2$. The accuracy of the simulated inundation results for heavy rainfall in 2010 and 2011 are 0.61 and 0.57 in POD index, respectively. 20 inundation scenarios responding on rainfall scenarios with 10~200 mm interval are produced for 60 and 120 minutes of rainfall duration. When the inundation damage thresholds are defined as pre-occurrence stage, occurrence stage to $0.01km^2$, 0.01 to $0.1km^2$, and $0.1km^2$ or more in area with a depth of 0.5 m or more, rainfall thresholds responding on each inundation damage threshold results in: 0 to 20 mm, 20 to 50 mm, 50 to 80 mm, and 80 mm or more in the rainfall duration 60 minutes and 0 to 30 mm, 30 to 70 mm, 70 to 110 mm, and 110 mm or more in the rainfall duration 120 minutes. Rainfall thresholds as a trigger of urban inundation damage can be used to form an inundation risk matrix. It is expected to be used for urban flood impact forecasting.

• Journal of Korea Technology Innovation Society
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• v.21 no.2
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• pp.818-837
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• 2018

Technological innovation has inherent difficulties, largely due to the uncertainties of technology. Thus, the forecasting methodology to reduce the risk of uncertainty in the innovation process has been presented both in quantitative and qualitative fields. On the other hand, big data and artificial intelligence have attracted great interest recently, and deep learning, which is one of the algorithms of AlphaGo, is showing excellent performance. In this study, deep learning methodology was applied to the prediction of innovation performance. To make the prediction model, we used KIS 2016 data. The input factors were importance of information source and innovation objectives and the output factor was innovation performance index, which was calculated for this study. As a result of the analysis, it can be confirmed that the accuracy of prediction is improved compared with the previous studies. As learning progressed, the degree of freedom of prediction also improved.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.