• Korean Journal of Adult Nursing
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• v.20 no.3
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• pp.489-499
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• 2008

Purpose: The purpose of this study was to assess the importance and contribution of 9 nursing outcomes and their indicators that could be applied to cerebrovascular patients. Methods: Data were collected from 175 neurosurgical nurses working at two university affiliated hospitals and five secondary hospitals located in Gwang-ju. The Fehring method was used to estimate outcome content validity(OCV) and outcome sensitivity validity(OSV) of nursing outcomes and their indicators. Stepwise regression was used to evaluate relationship between outcome and its indicators. Results: The core outcomes identified by the OCV were Tissue Perfusion: Cerebral, Nutritional Status, Neurological Status, and Wound Healing: Primary Intention, whereas highly supportive outcomes identified by the OSV were Oral Health, Self-Care: ADL, and Nutritional Status. All the critical indicators selected for Fehring method were not included in stepwise regression model. By stepwise regression analysis, the indicators explained outcomes from 19% to 52% in importance and from 21% to 45% in contribution. Conclusion: This study identified core and supportive outcomes and their indicators which could be useful to assess the physical status of cerebrovascular patients. Further research is needed for the revision and development of nursing outcomes and their indicators at neurological nursing area.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.6
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• pp.628-641
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• 2014

Stepwise efficiency improvement in data envelopment analysis (DEA)-based benchmarking is a realistic and effective method by which inefficient decision making units (DMUs) can choose benchmarks in a stepwise manner and, thereby, effect gradual performance improvement. Most of the previous research relevant to stepwise efficiency improvement has focused primarily on how to stratify DMUs into multiple layers and how to select immediate benchmark targets in leading levels for lagging-level DMUs. It can be said that the sequence of benchmark targets was constructed in a myopic way, which can limit its effectiveness. To address this issue, this paper proposes an optimization approach to the construction of a sequence of benchmarks in DEA-based benchmarking, wherein two optimization criteria are employed : similarity of input-output use patterns, and proximity of input-output use levels between DMUs. To illustrate the proposed method, we applied it to the benchmarking of 23 national universities in South Korea.

• Journal of Microbiology and Biotechnology
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• v.10 no.4
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• pp.476-481
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• 2000

A linear correlation, by the stepwise multiple regression analysis, was found between the sensory test of Sigumjang aroma and the gas chromatographic data which were transformed with logarithm. GC data is the most objective method to evaluate Sigumjang aroma. A multiple correlation coefficient and a determination coefficient of more than 0.9 were obtained at the 9th and 13th steps, respectively. At step 31, the coefficient of determination level of 0.95 was attained. The accuracy of its estimation became higher as the number of the variables entered into the regression model increased. Over 90% of the Sigumjang aroma was explained by 13 compounds indentified on GC. The contributing proportion of the peak 26 was the highest followed by peaks 57 (9.27%), 29 (7.51%), 54 (6.01%), 8 (5.99%), 49 (4.97%), and 13 (4.11%).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.6
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• pp.2840-2850
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• 2011

Programmers can use the Hardware-Software Co-Designing, in which they design a hardware system and software system simultaneously and then unify them, to design an embedded system. This study analyzes the problems of designing an embedded system and suggests applying the system-level design technique, which uses SpecC technology, as a solution to the problems. Also, it suggests systematic and concrete stepwise refinement by applying Refactoring technology in software engineering as a way to make the use of system-level stepwise refinement.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.31-39
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• 2015

This paper deals with a method for estimating variance components on the basis of projections under the assumption of random effects model. It discusses how to use projections for getting sums of squares to estimate variance components. The use of projections makes the vector subspace generated by the model matrix to be decomposed into subspaces that are orthogonal each other. To partition the vector space by the model matrix stepwise procedure is used. It is shown that the suggested method is useful for obtaining Type I sum of squares requisite for the ANOVA method.

• IE interfaces
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• v.22 no.3
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• pp.252-262
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• 2009

It is crucial to prevent low yields in the semiconductor industry. Since many factors affect variation in yield and they are deeply related, preventing low yield is difficult. There have been substantial researches in the field of yield prediction. Many researchers had used the statistical methods. Many studies have shown that artificial neural network (ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance some problems such as over-fitting and poor explanatory power arise. In order to overcome these limitations, a relatively new machine learning technique, support vector machine (SVM), is introduced to classify the yield. SVM is simple enough to be analyzed mathematically, and it leads to high performances in practical applications. This study presents a new efficient classification methodology, Stepwise-SVM (SSVM), for detecting high and low yields. SSVM is step-by-step adjustment of parameters to be precisely the classification for actual high and low yield lot. The objective of this paper is to examine the feasibility of SVM and SSVM in the yield classification. The experimental results show that SVM and SSVM provides a promising alternative to yield classification for the field data.

• Korean Journal of Medicinal Crop Science
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• v.21 no.3
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• pp.204-212
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• 2013

In general, stepwise hot steaming process is known to be effective in improving its biological activities; however, not much employed in processing Codonopsis lanceolata due to its hardness. The complex processed C. lanceolata showed highest free radical scavenging acitivity as 45.21%. Total phenol and flavonoid content were of complex processed C. lanceolata higher than conventional extract and alone steaming process. It was showed the lower melanogenesis rate on melanin production test by B16F10 cells as 27.46%. High inhibitory of tyrosinase was also measured as 28.61% by adding steamed Codonopsis lanceolata extracts by high pressure extraction of 1.0 $mg/m{\ell}$. And anti-wrinkle activity were 39.08%. In comparing phenolic acids profiles in the extract, in general higher amounts of polyphenol were obtained possibly by easy release of active components during thermal processing, which results in better antioxidant activities than that of general extract. This findings can also be supported by result that the extract by steaming process showed better activities than the general extraction extract.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.4 s.56
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• pp.159-169
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• 2009

In this study, static loading tests and numerical analyses of the composite beam strengthened by multi-stepwise thermal prestressing method were carried out to evaluate the prestressing effect of the thermal prestressing prestress and the sectional effect of the installed cover-plate on the increase in the load-carrying capacity of composit beam. From this study, the strengthening method using multi-stepwise thermal prestressing method (TPSM) can be applied to reduce the deflection of the composite beam as well as to strengthening the composite beam by inducing the prestress in case of the occurrence in the large deflection by the insufficiency of the section properties of the composite beam. because of the expectation of the increase in the yield load and the sectional properties of the composite beam.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.347-354
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• 2015

This paper deals with a method for intrablock anlaysis of balanced incomplete block designs on the basis of projections under the assumption of mixed effects model. It shows how to construct a model at each step by the stepwise procedure and discusses how to use projection for the analysis of intrablock. Projections are obtained in vector subspaces orthogonal to each other. So the estimates of the treatment effects are not affected by the block effects. The estimability of a parameter or a function of parameters is discussed and eigenvectors are dealt for the construction of estimable functions.

• Journal of Information Technology Applications and Management
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• v.20 no.4
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• pp.221-234
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• 2013

In this paper, we report bankruptcy risk level forecasting result for automobile parts manufacturing industry. With the premise that upstream supply risk and downstream demand risk could impact on automobile parts industry bankruptcy level in advance, we draw upon industry input-output table to use the economic indicators which could reflect the extent of supply and demand risk of the automobile parts industry. To verify the validity of each economic indicator, we applied simple linear regression for each indicators by varying the time lag from one month (t-1) to 12 months (t-12). Finally, with the valid indicators obtained through the simple regressions, the composition of valid economic indicators are derived using stepwise linear regression. Using the monthly automobile parts industry bankruptcy frequency data accumulated during the 5 years, R-square values of the stepwise linear regression results are 68.7%, 91.5%, 85.3% for the 3, 6, 9 months time lag cases each respectively. The computational testing results verifies the effectiveness of our approach in forecasting bankruptcy risk forecasting of the automobile parts industry.