• Korean Journal of Rural Living Science
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• v.6 no.1
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• pp.11-24
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• 1995

The new textile materials may be defined as textile materials different from already existing ones in the physical and chemical structure, manufacturing process, or end-use property. The present time what is called the post-industrial society is characterized by rapid change and new technology. Also, textile materials have been changed rapidly and diversely in the post-industrial society than in any other periods. The study aimed to analyze the trend of new tektite materials development in Korea and to forecast the development trend in the future. To investigate the trend of new textile materials, various written materials and informations were collected from the manufacturers, textile related periodicals, and research journals, and they were analyzed. The period of analysis was from January 1992 to May 1995. The results of this research are as followings : (1) Mixed textile materiasl such as bicomponent fiber, blended yam and blended fabric were increased. (2) High technology has an important effect upon new textile material development. (3) functional textile materials were increased (4) The high value-added products were increased. (5) The naturalized textile materials were increased.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3097-3100
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• 2015

Projections of cancer cases are particularly useful in developing countries to plan and prioritize both diagnostic and treatment facilities. In the prediction of cancer cases for the future period say after 5 years or after 10 years, it is imperative to use the knowledge of past time trends in incidence rates as well as in population at risk. In most of the recently published studies the duration for which the time trend was assessed was more than 10 years while in few studies the duration was between 5-7 years. This raises the question as to what is the optimum time period which should be used for assessment of time trends and projections. Thus, the present paper explores the suitability of different time periods to predict the future rates so that the valid projections of cancer burden can be done for India. The cancer incidence data of selected cancer sites of Bangalore, Bhopal, Chennai, Delhi and Mumbai PBCR for the period of 1991-2009 was utilized. The three time periods were selected namely 1991-2005; 1996-2005, 1999-2005 to assess the time trends and projections. For the five selected sites, each for males and females and for each registry, the time trend was assessed and the linear regression equation was obtained to give prediction for the years 2006, 2007, 2008 and 2009. These predictions were compared with actual incidence data. The time period giving the least error in prediction was adjudged as the best. The result of the current analysis suggested that for projections of cancer cases, the 10 years duration data are most appropriate as compared to 7 year or 15 year incidence data.

• Convergence Security Journal
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• v.7 no.2
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• pp.49-55
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• 2007

Software failure time presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing. For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. From the subdivision of this analysis, new attemp needs the side of the quality control. In this paper, we discuss process capability analysis using process capability indexs. Because of software failure interval time is pattern of nonnegative value, instead of capability analysis of suppose to normal distribution, capability analysis of process distribution using to Box-Cox transformation is attermpted. The used software failure time data for capability analysis of process is SS3, the result of analysis listed on this chapter 4 and 5. The practical use is presented.

• Asian-Australasian Journal of Animal Sciences
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• v.11 no.4
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• pp.363-367
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• 1998

This study has objectives of to estimate the genetic and phenotypic trend for growth in a closed herd of Landrace $\times$ desi crossbreds. The possibility of early selection of boars was also investigated in order to reduce generation interval and thus, to enhance response per year in selection programmes. The data originated from Livestock Production Research (Pigs), Indian Veterinary Research Institute (IVRI), Izatnagar (UP), India - a unit of All India Coordinated research Project on Pigs (AICRP on Pigs). Data consisted of 891 crossbred piglets, progeny of 29 boars. The piglets were born in 132 parities of 72 sows between 8 years from 1987 to 1994. Records on weight at birth, at 2 weeks interval upto 8 weeks of age (Wl, W2, ${\cdots}\;{\cdots}$ W8) and at 16th week (W16) were used in this investigation. BLLTP estimates of the sires were computed. Breeding value of each sire was estimated as twice of sire and sire group solutions. Phenotypic trend was estimated as regression of weight performance on year. Genetic trend was computed by estimating regression of breeding value of sires on time. Average body weights ranged from 0.92 kg (W1) to 18.95 kg (W16) and showed a continuous increase over age. Heritabilities of the weight at 4th and 6th week were medium (0.29 and 0.14). Rest of the weights were highly heritable. The product moment and rank, both correlations were high between breeding value for W6 and W16 (0.68 and 0.70). This shows that sire selection for W6 can be successfully implemented in order to achieve sufficient genetic improvement in growth. Phenotypic trend was positive at all ages. The phenotypic regression coefficient ranged from 0.02 kg at birth to 0.40 kg at 16 weeks. Genetic trend was also positive. The regression coefficients of average breeding value of sires on time showed a range of 1.471 kg (0.021 to 1.492 kg) for different weights. These coefficients were significant and higher than their corresponding phenotypic regression coefficient.

• Journal of Environmental Science International
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• v.26 no.2
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• pp.221-230
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• 2017

To determine the effect of air pollution reduction policies, the long-term trend of air pollutants should be analyzed. Kolmogorov-Zurbenko (KZ) filter is a low-pass filter, produced through repeated iterations of a moving average to separate each variable into its temporal components. The moving average for a KZ(m, p) filter is calculated by a filter with window length m and p iterations. The output of the first pass subsequently becomes the input for the next pass. Adjusting the window length and the number of iterations makes it possible to control the filtering of different scales of motion. To break down the daily mean $PM_{10}$ into individual time components, we assume that the original time series comprises of a long-term trend, seasonal variation, and a short-term component. The short-term component is attributable to weather and short-term fluctuations in precursor emissions, while the seasonal component is a result of changes in the solar angle. The long-term trend results from changes in overall emissions, pollutant transport, climate, policy and/or economics. The long-term trend of the daily mean $PM_{10}$ decreased sharply from $59.6ug/m^3$ in 2002 to $44.6ug/m^3$ in 2015. This suggests that there was a long-term downward trend since 2005. The difference between the unadjusted and meteorologically adjusted long-term $PM_{10}$ is small. Therefore, we can conclude that $PM_{10}$ is unaffected by the meteorological variables (total insolation, daily mean temperature, daily mean relative humidity, daily mean wind speed, and daily mean local atmospheric pressure) in Busan.

• Journal of Wetlands Research
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• v.6 no.4
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• pp.35-43
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• 2004

The non-parametric Mann-Kendall(MK) statistical test has been widely used to assess the significance of trend in hydrologic time series. The test requires sample data should be serially independent. If sample data is serially correlated, the presence of serial correlation in a time series will affect the test ability for trend analysis. So, we would like to use the modified MK test which uses the effective sample size(ESS) to eliminate the effect of serial correlation in a series. This study investigates the ability of ESS to eliminate the influence of serial correlation of MK test by Monte Carlo simulation and by real series. As the results, MK test shows the increase of trend rate as the serial correlation is increased but the modified MK test shows ESS can eliminate the serial correlation for trend analysis. Therefore we confirmed the modified MK test is a very useful tool for trend analysis.

• Journal of the Korean association of regional geographers
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• v.17 no.4
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• pp.432-442
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• 2011

Climatic variables such as temperature and precipitation tend to vary both in space and in time simultaneously. Thus, it is necessary to include space-time autocorrelation into conventional spatial interpolation methods for reliable time-series mapping. This paper introduces and applies space-time variogram modeling and space-time kriging to generate time-series temperature maps using hourly Automatic Weather System(AWS) temperature observation data for a one-month period. First, temperature observation data are decomposed into deterministic trend and stochastic residual components. For trend component modeling, elevation data which have reasonable correlation with temperature are used as secondary information to generate trend component with topographic effects. Then, space-time variograms of residual components are estimated and modelled by using a product-sum space-time variogram model to account for not only autocorrelation both in space and in time, but also their interactions. From a case study, space-time kriging outperforms both conventional space only ordinary kriging and regression-kriging, which indicates the importance of using space-time autocorrelation information as well as elevation data. It is expected that space-time kriging would be a useful tool when a space-poor but time-rich dataset is analyzed.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.10 no.2
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• pp.41-47
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• 2006

The Renewal process and the Non-homogeneous Poisson process (NHPP) process are probably the most popular models for describing the failure pattern of repairable systems. But both these models are based on too restrictive assumptions on the effect of the repair action. For these reasons, several authors have recently proposed point process models which incorporate both renewal type behavior and time trend. One of these models is the Modulated Power Law Process (MPLP). The Modulated Power Law Process is a suitable model for describing the failure pattern of repairable systems when both renewal-type behavior and time trend are present. In this paper we propose Bayes estimation of the next failure time after the system has experienced some failures, that is, Mean Time Between Failure for the MPLP model. Numerical examples illustrate the estimation procedure.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.199-205
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• 2005

In general, simple correspondence analysis has handled mainly correspondence relations between the row and column categories but can not display the trends of their change over the time. For solving this problem, we will propose DSCA(Dynamic Simple Correspondence Analysis) of transition matrix data using supplementary categories in this study, Moreover, DSCA provides its trend of the change for the future by predicting and displaying trend toward the change from a standard point of time to the next.

• Journal of the Korean Statistical Society
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• v.15 no.1
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• pp.31-45
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• 1986

It is often necessary to obtain some run orders in factorial designs which have a small number of factor level changes and a small linear time trend. In this paper we propose an algorithm to find optimal or near-optimal run orders for $2^4, 2^5, 3^2$ and $2\cdot 3^2$ factorial designs under the criterion that the number of factor level changes and the linear time trend should be simultaneously small.