• Journal of Korean Institute of Industrial Engineers
• /
• v.29 no.2
• /
• pp.179-189
• /
• 2003

Gate poly-silicon critical dimension is a prime characteristic of a metal-oxide-semiconductor field effect transistor. It is important to achieve the uniformity of gate poly-silicon critical dimension in order that a semiconductor device has acceptable electrical test characteristics as well as a semiconductor wafer fabrication process has a competitive net-die-per-wafer yield. However, on gate poly-silicon critical dimension, the complexity associated with a semiconductor wafer fabrication process entails hierarchical variance components according to run-to-run, wafer-to-wafer and even die-to-die production unit changes. Specifically, estimates of the hierarchical variance components are required not only for disclosing dominant sources of the variation but also for testing the wafer-level uniformity. In this paper, two experimental designs, a two-stage nested design and a randomized complete block design are considered in order to estimate the hierarchical variance components. Since gate poly-silicon critical dimensions are collected from fixed die positions within wafers, a factor representing die positions can be regarded as fixed in linear statistical models for the designs. In this context, the two-stage nested design also checks the wafer-level uniformity taking all sampled runs into account. In more detail, using variance estimates derived from randomized complete block designs, Duncan's multiple range test examines the wafer-level uniformity for each run. Consequently, a framework presented in this study could provide guidelines to practitioners on estimating the hierarchical variance components and testing the wafer-level uniformity in parallel for any characteristics concerned in semiconductor wafer fabrication processes. Statistical analysis is illustrated for an experimental dataset from a real pilot semiconductor wafer fabrication process.

• Journal of Preventive Medicine and Public Health
• /
• v.49 no.2
• /
• pp.134-138
• /
• 2016

Objectives: Season of birth, an exogenous indicator of early life environment, has been related to higher risk of adverse psychiatric outcomes but the findings for Alzheimer's disease (AD) have been inconsistent. We investigated whether the month or season of birth are associated with AD. Methods: A nationwide nested case-control study including all community-dwellers with clinically verified AD diagnosed in 2005 to 2012 (n=70 719) and up to four age- sex- and region of residence-matched controls (n=282 862) residing in Finland. Associations between month and season of birth and AD were studied with conditional logistic regression. Results: Month of birth was not associated with AD (p=0.09). No strong associations were observed with season (p=0.13), although in comparison to winter births (December-February) summer births (June-August) were associated with higher odds of AD (odds ratio, 1.03; 95% confidence interval, 1.00 to 1.05). However, the absolute difference in prevalence in winter births was only 0.5% (prevalence of those born in winter were 31.7% and 32.2% for cases and controls, respectively). Conclusions: Although our findings do not support the hypothesis that season of birth is related to AD/dementia risk, they do not invalidate the developmental origins of health and disease hypothesis in late-life cognition. It is possible that season does not adequately capture the early life circumstances, or that other (postnatal) risk factors such as lifestyle or socioeconomic factors overrule the impact of prenatal and perinatal factors.

• Journal of Korea Spatial Information System Society
• /
• v.9 no.2
• /
• pp.25-34
• /
• 2007

Existing approaches that select an order for the join of three or more data streams have always used the simple heuristics. For their disadvantage - only one factor is considered and that is join selectivity or arrival rate, these methods lead to poor performance and inefficiency In some applications. The graph-based sliding window multi -join algorithm with optimal join sequence is proposed in this paper. In this method, sliding window join graph is set up primarily, in which a vertex represents a join operator and an edge indicates the join relationship among sliding windows, also the vertex weight and the edge weight represent the cost of join and the reciprocity of join operators respectively. Then the optimal join order can be found in the graph by using improved MVP algorithm. The final result can be produced by executing the join plan with the nested loop join procedure, The advantages of our algorithm are proved by the performance comparison with existing join algorithms.

• Journal of Fisheries and Marine Sciences Education
• /
• v.27 no.4
• /
• pp.1184-1193
• /
• 2015

Monitoring for megalocytivirus infection was conducted for ten months from March to December in 2013 in 15 aquatic farms culturing, red sea bream, rock bream, rock fish and black sea bream around Tongyoung coastal area in Korea, to assess spatial and temporal variability of detection prevalence, and to explore possible links with seawater temperature. In nested-PCR targeted major capsid protein (MCP) gene, asymptomatic megalocytivirus infection was detected in the externally healthy farmed fish with a significant prevalence in range from 0 to 58.3% for ten months. Higher prevalence of megalocytivirus (46.7% - 57.1%) was observed in high water temperature season from September to November than that in other months with lower prevalence of 0.0% to 20.0%. Even though an acute infection of megalocytivirus was occurred in rock bream (positive in the first PCR) with high mortality in one of fifteen farms, there was no expansion or transmission of the disease to the rock fish and red sea bream culturing in net cage just proximal to the rock bream cage in which disease outbreaked. Nucleotide sequence analysis of the cloned MCP gene isolated asymptomatically infected rock fish revealed that the megalocytivirus in this study was clustered together with the rock bream iridovirus (RBIV) under the subgroup II of the genus megalocytivirus (Iridoviridae), which is known to be the major megalocytivirus strain in Korea. The typical histopathological signs were not found in the spleen of rock fish asymptomatically infected by megalocytivirus. Experimental infection of rock bream with the spleen homogenate of the rock fish infected asymptomatically did not induce any mortality unlike the homogenate of infected rock bream with hih mortlity. However, these results may suggest that the asymptomatic infection of megalocytivirus in other fish species can be a potential risk threatening aquaculture industries as a transmission factor of megalocytivirus to susceptible fish species, especially rock bream.

• Journal of the Korea Safety Management & Science
• /
• v.13 no.2
• /
• pp.195-202
• /
• 2011

The study focuses on the Repeated Measurements Design (RMD) which observations are periodically made for identical subjects within definite time periods. One of the purposes of this design is to monitor and keep track of replicated records within regular period over years. This paper also presents the classification models of RMD that is developed according to the number of factors in Between-Subject (BS) variates and Within-Subject (WS) variates. The types of models belong to each number of factors: One factor is 0BS 1WS. Two factors are 1BS 1WS and 0BS 2WS. Three factors are 1BS 2WS and 2BS 1WS. Lastly, the four factors include model of 2BS 2WS In addition, the study explains the generation mechanism of models for RMD using Generalizability Design (GD). GD is a useful method for practitioners to identify linear model of experimental design, since it generates a Venn diagram. Lastly, the research develops three types of 1BS 2WS RMDs with crossed factors and nested factors. Those are random models, mixed models and fixed models and they are presented by using Generalizability Design, $(S:A{\times}B){\times}C$. Moreover, the example of applications and its implementation steps of models developed in the study are presented for better comprehension.

• Journal of Korean Society of Transportation
• /
• v.22 no.6
• /
• pp.7-16
• /
• 2004

In mode choice decision, travelers consider not only travel time but also reliability of its modes. In this paper, reliability was expressed in terms of standard deviation and maximum delay that were measured based on triangular distribution. In order to estimate value of time and value of reliability, the Multinomial and Nested Logit models were used. The analysis results revealed that reliability is an important factor affecting mode choice decisions. Elasticity is used to estimate the impacts of the different policies and system improvements for water transportation mode. Among these policies, decision maker can assess and select the best alternative by doing the benefit and cost analysis based on a new market share, the value of time, and the value of reliability. Finally, a set of promising policies and system improvement of the water transportation were proposed.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2005.05a
• /
• pp.975-982
• /
• 2005

The purpose of the Gage R&R study is to determine whether a measurement system is adequate for monitoring a process. If the measurement system variation is small relative to the process variation, then the measurement system is deemed 'adequate'. The sources of variation associated with the measurement system are compared using an analysis of variance (ANOVA) model, in general. A typical ANOVA model used in a standard Gage R&R study is the two-factor random effect model. Then, the ANOVA partitions the total variation into three categories: repeatability, reproducibility, part variation. However, if the process variation possesses the between group variation, within group variation, and within-part variation, these variations can cause the measurement system evaluation to provide misleading results. That is, in the standard Gage R&R study these variations affect the estimate of repeatability, reproducibility, or both. This paper presents a four-factor nested factorial ANOVA model which explicitly considers these variations for the Gage R&R study. The variance component estimates are derived by setting the EMS equations equal to the corresponding mean square from the ANOVA table and solving. And the proposed model is compared with the standard Gage R&R model.

• IE interfaces
• /
• v.18 no.4
• /
• pp.444-453
• /
• 2005

The purpose of the Gage R&R study is to determine whether a measurement system is adequate for monitoring a process. If the measurement system variation is small relative to the process variation, then the measurement system is deemed "adequate". The sources of variation associated with the measurement system are compared using an analysis of variance (ANOVA) model, in general. A typical ANOVA model used in a standard Gage R&R study is the two-factor random effect model. Then, the ANOVA partitions the total variation into three categories: repeatability, reproducibility, part variation. However, if the process variation possesses the between group variation, within group variation, and within part variation, these variations can cause the measurement system evaluation to provide misleading results. That is, in the standard Gage R&R study these variations affect the estimate of repeatability, reproducibility, or both. This paper presents a four-factor nested factorial ANOVA model which explicitly considers these variations for the Gage R&R study. The variance component estimators are derived by setting the EMS equations equal to the corresponding mean square from the ANOVA table and solving. And the proposed model is compared with the standard Gage R&R model.

• Journal of Korean Society of Transportation
• /
• v.7 no.1
• /
• pp.19-34
• /
• 1989

In general, Shopping centers are located in the center of transportation, which induce more traffic concentration than other facilities. As a result, it becomes to one of the aggravating factors of urban ＆ transportation problem such as traffic conjestion, parking problems. Thus, in planning shopping-center, it is desirable that consumer\`s trip pattern is should lie more carefully analyzed in order to alleviate the above problems. This study is aimed at finding the characteristics of shopping tripes and analyzing the choice behavior of shopping center and mode-to-shop. This study has developed a nested logit model in which consumers choose shopping center and mode-to-shop with a sequential structure. The model was estimated using household data from the 1989 May, Seoul metropolitan area and the 18 alternatives of shopping center and 5 mode alternatives. The estimated model have been evaluated and it may be concluded that this model for shopping trips is effective and fesible. The conclusion of this study are as fellows. 1. Out-of-vehicle travel time is more important factor affecting behavior of mode choice than in-vehicle travel time. 2. All of direct-elasticities for mode with respect to the travel time is more elastic than travel cost. 3. Accessibility to shop is more important factor affecting the choice of shopping center than parking space. 4. The value of out-of-vehicle travel time exceeds the value of in-vesicle travel time by 1.64 times.

• Journal of Genetic Medicine
• /
• v.9 no.2
• /
• pp.78-83
• /
• 2012

Purpose: The aim of this nested case-control study was to investigate the association between hepatocyte growth factor (HGF) concentrations in maternal plasma and the risk of developing preeclampsia. Materials and Methods: Plasma HGF concentration were measured in 52 women who subsequently developed preeclampsia and 104 normal pregnant women at the time of genetic amniocentesis (15-20 weeks) by enzyme-linked immunosorbent assay. Results: Maternal plasma HGF concentrations were significantly higher in women with subsequent preeclampsia (median: 737.8 ng/mL vs. 670.4 ng/mL, P=0.003) than in normal controls. However, HGF concentrations were not significantly different between subgroups by preeclamptic complications. After adjusting for potential confounding factors, women with HGF concentrations ${\geq}702.5ng/mL$ had a 3.2-fold increased risk (95% CI 2.7-5.4, P<0.001) of subsequent development of preeclampsia compared with women with HGF concentrations <702.5 ng/mL. Conclusion: Elevated maternal plasma HGF concentrations in the early second-trimester are associated with an increased risk of developing preeclampsia.