• Asian Oncology Nursing
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• v.11 no.2
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• pp.163-170
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• 2011

Purpose: The purpose of this study was to analyze the research papers published in three nursing journals to suggest the direction for Journal of Korean Oncology Nursing (JKON). Methods: To compare JKON with Journal of Korean Academic Society of Nursing Education and Cancer Nursing, all the research papers published in those three journals, 2010 were reviewed using an analysis criteria developed by the researchers, focusing on type of research, characteristics of authors and subjects, research design, data collection and analysis methods, sample size estimation, and ethical considerations regarding data collection. Results: JKON lacked research papers which were supported by research funds, produced by multidisciplinary teams, addressing cancer survivors or patients with metastatic cancers, and written in qualitative methodologies. However, JKON showed higher ratio of research papers than the other two journals which were adapted from thesis or dissertations, describing sample size estimation process precisely, and participating subjects diagnosed with various cancers. Conclusion: The study found out that JKON is presenting well the area of oncology nursing in Korea and also has several weak points that need to be improved. The study therefore suggested several recommendations for the JKON to take the professional and global leader roles.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2D
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• pp.103-110
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• 2012

This study presents O/D based emission estimation model and methodology under cold- and hot-start conditions. Contrasting with existing link-based model, new model is able to estimate cold-start emissions with actual traffic characteristics. The results of the case study with new model show similar amount of emission with existing model under hot-start conditions, but five times much more than existing model under cold-start conditions. The annual social benefit estimated by this model is 56.2 hundred million won, which is 48% higher than the result from existing model. It means current green transportation policies are undervalued in terms of air quality improvement. Therefore, New model is expected to improve the objectivity of air quality evaluation results regarding green transportation policies and be applied in various transportation-environment policies.

• Tunnel and Underground Space
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• v.13 no.2
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• pp.87-99
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• 2003

Geostatistics is defined as the theory of modeling of regionalized variables and is an efficient and elegant methodology for estimation and uncertainty evaluation from limited spatial sample data. In this study, we have made a theoretical comparison between kriging estimation and geostatistical simulation methods. Kriging methods do not preserve the histogram of original data nor their spatial structure, and also provide only an incomplete measure of uncertainty when compared to the simulation methods. A practical procedure of geostatistical simulation is suggested in this study and the technique is demonstrated through an application, in which it was used to identify the spatial distribution of RMR as well as to evaluate the spatial uncertainty. It is concluded that the geostatistical simulation is the appropriate method to quantify the spatial uncertainty of geotechnical variables such as RMA. Therefore, the results from the simulation can be used as useful information for designer's considerations in decision-making under various geological conditions as well as the related terms of contract.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.694-702
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• 2014

Since Greenhouse Gas Inventory & Research Center (GIR) of Korea was founded in 2010, the annual greenhouse gas inventory reports, one of the collections of GIR's major affairs, have been published from 2012. In the reports many items related to greenhouse gas emission quantities are included, but among them uncertainty values are replaced to basic values which IPCC guideline suggests. Even though IPCC guideline suggests the equations of each Tier level in details, the guideline recommends developing nation's own methodology on uncertainty which is closely related to statistical problems such as the estimation of a probability density function or Monte carlo methods. In the road transportation sector the emissions have been calculated by Tier 1 but the uncertainties have not been reported. This study introduce a bootstrap technique and Monte carlo method to estimates annual emission quantity and uncertainty, given activity data and emission factors such as annual traveled distances, fuel efficiencies and emission coefficients.

• Journal of Food Hygiene and Safety
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• v.18 no.3
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• pp.107-112
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• 2003

Probabilistic exposure assessment has been recognized as an important tool in microbial risk assessment, because of obtained the desired results to characterize of variability and uncertainty associated with the microbial hazards. In addition, it will be provided much more actuality information than the point-estimate approaches. In this study, we present methodology using mathematical probability distribution in exposure assessment and estimating of contamination level of Listeria monocytogenes in meat and meat products as a case study. The result of estimation contaminatin level was mean ($50^{th}$ percentile) -4.08 Log CFU/g minimum ($5^{th}$ percentile) -4.88 Log CFU/g, maximum ($95^{th}$ percentile) -3.56 Log CFU/g.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.246-252
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• 2017

In the defense acquisition, a company's goal is to maximize profits, and the government's goal is to allocate budgets efficiently. Each year, the government estimates the ratio of indirect cost sector to defense companies, and estimates the ratio to be applied when calculating cost of the defense articles next year. The defense industry environment is changing rapidly, due to the increasing trend of defense acquisition budgets, the advancement of weapon systems, the effects of the 4th industrial revolution, and so on. As a result, the cost structure of defense companies is being diversifying. The purpose of this study is to find an alternative that can enhance the rationality of the current methodology for estimating the ratio of indirect cost sector of defense companies. To do this, we conducted data analysis using the R language on the cost data of defense companies over the past six years in the Defense Integrated Cost System. First, cluster analysis was conducted on the cost characteristics of defense companies. Then, we conducted a regression analysis of the relationship between direct and indirect costs for each cluster to see how much it reflects the cost structure of defense companies in direct labor cost-based indirect cost rate estimates. Lastly a new ratio prediction model based on regularized regression analysis was developed, applied to each cluster, and analyzed to compare performance with existing prediction models. According to the results of the study, it is necessary to estimate the indirect cost ratio based on the cost character group of defense companies, and the direct labor cost based indirect cost ratio estimation partially reflects the cost structure of defense companies. In addition, the current indirect cost ratio prediction method has a larger error than the new model.

• Journal of the Korean Geographical Society
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• v.51 no.2
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• pp.219-233
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• 2016

In social science fields, statistical models are used almost exclusively for causal explanation, and explanatory modeling has been a mainstream until now. In contrast, predictive modeling has been rare in the fields. Hence, we focus on constructing the predictive non-parametric model, instead of the explanatory model. Gangnam-gu, Seoul was chosen as a study area and we collected single-family house sales data sold between 2011 and 2014. We applied non-parametric models proposed in machine learning area including generalized additive model(GAM), random forest, multivariate adaptive regression splines(MARS) and support vector machines(SVM). Models developed recently such as MARS and SVM were found to be superior in predictive power for house price estimation. Finally, spatial autocorrelation was accounted for in the non-parametric models additionally, and the result showed that their predictive power was enhanced further. We hope that this study will prompt methodology for property price estimation to be extended from traditional parametric models into non-parametric ones.

• Korean Journal of Construction Engineering and Management
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• v.10 no.5
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• pp.104-112
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• 2009

After the middle of 90's, the eco rivers maintenance enterprise was propelled about city rivers. The environmental function is more emphasized because of revising the rivers law enforced at 2008.4. Also, the field of application is being magnified. It is difficult to apply that the conceptual public work expense estimating model of the rivers which adjusts a focus at open channel rivers excepts the small-scale rivers maintenance public work. The research presents a eco rivers public work conceptual public work expense estimating model frame work. It suits the change of the rivers environmental renewal construction paradigm. It develops the conceptual public work expense estimating plan of the rivers at the planning phase using the collection and analysis of the data. As a result, riffle, spur dyke, stepping stones, fish way and etc are added. Consequently, it brings the hydrophilic function is considered seriously conceptual public work expense estimating model of the eco rivers.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5A
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• pp.479-486
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• 2008

In this paper, an efficient training symbol design with m-sequence is proposed for the MIMO-OFDM based next generation wireless transmission system which supports gigabits per second data rate. In the traditional blute force method, the preamble design is based on the case by case comparison with the system requirements. This paper discusses a training symbol design methodology for the MIMO-OFDM system based on the m-sequence which has been widely used in the spread spectrum communication areas due to its good correlation characteristics. Also the step-by-step design and performance verification method within the limited search space is discussed. The proposed method targets the design of the training symbol which satisfies system requirements for the packet based MIMO-OFDM wireless communication system including automatic gain control(AGC), timing synchronization, frequency and sampling offset estimation, and MIMO channel estimation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1772-1781
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• 2017

In this study, the design methodology for alleviating the overfitting problem of Polynomial Neural Networks(PNN) is realized with the aid of two kinds techniques such as L2 regularization and Sum of Squared Coefficients (SSC). The PNN is widely used as a kind of mathematical modeling methods such as the identification of linear system by input/output data and the regression analysis modeling method for prediction problem. PNN is an algorithm that obtains preferred network structure by generating consecutive layers as well as nodes by using a multivariate polynomial subexpression. It has much fewer nodes and more flexible adaptability than existing neural network algorithms. However, such algorithms lead to overfitting problems due to noise sensitivity as well as excessive trainning while generation of successive network layers. To alleviate such overfitting problem and also effectively design its ensuing deep network structure, two techniques are introduced. That is we use the two techniques of both SSC(Sum of Squared Coefficients) and $L_2$ regularization for consecutive generation of each layer's nodes as well as each layer in order to construct the deep PNN structure. The technique of $L_2$ regularization is used for the minimum coefficient estimation by adding penalty term to cost function. $L_2$ regularization is a kind of representative methods of reducing the influence of noise by flattening the solution space and also lessening coefficient size. The technique for the SSC is implemented for the minimization of Sum of Squared Coefficients of polynomial instead of using the square of errors. In the sequel, the overfitting problem of the deep PNN structure is stabilized by the proposed method. This study leads to the possibility of deep network structure design as well as big data processing and also the superiority of the network performance through experiments is shown.