• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.2
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• pp.63-75
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• 2018

This study analyzed the long-term linear trends of the sea surface height around the Korea marginal seas for the period of 1993~2016 by using quantile regression. We found significant difference about 2~3 mm/year for the linear trend between OLS (ordinary least square) and median (50%) quantile regression especially in the Yellow Sea, which is affected by extreme events. Each area shows different trend for each quantile (lower (1%), median (50%) and upper (99%)). Most areas of the Yellow Sea show increasing trend in both low and upper quantile, but significant "upward divergence tendency". This implies that significant increasing trend of upper quantile is higher than that of lower quantile in this area. Meanwhile, South Sea of Korea generally shows "upward convergence tendency" representing that increasing trend of upper quantile is lower than that of lower quantile. This study also confirmed that these tendencies can be eliminated by removing major tidal components from the harmonic analysis. Therefore, it is assumed that the regional characteristics are related to the long term change of tide amplitude.

• Journal of Soil and Groundwater Environment
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• v.14 no.5
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• pp.41-50
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• 2009

Water levels in groundwater monitoring wells of Jeju Island were analyzed using parametric and non-parametric trend analyses. Number of used monitoring wells in the analysis are 94 among totally 106 monitoring wells and the monitoring period is greater than single year, from 2001 to 2009. For the trend analysis, both parametric (linear regression) and nonparametric (Mann-Kendall trend test and Sen's trend test) methods were adopted. Results of the linear regression analysis on daily basis indicated that about 58.5% of the monitoring wells showed a decreasing trend, and analysis using monthly median indicated that about 79.8% showed a decreasing trend. The Mann-Kendall trend test and Sen's trend test with monthly median values in confidence levels of 95% and 99% showed the same analysis results. In confidence level of 95%, 32% were decreased, 3% were increased and the remains showed no trend. However, in confidence level of 99%, 16% were decreased, 2% were increased and the remains showed no trend. The largest decline rates of water levels were detected mainly at the coast of the northwestern and southwestern parts, which is expected to closely related to the increased pumping in the urban area and tourist resort.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.917-923
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• 2005

Scheduling is one of the main functions in construction project to determine the sequence of activities necessary to complete a project. The scheduling techniques provide important information crucial to a project's success. Highway construction project the paving activity can be considered a linear activity. Linear scheduling technique may be better suited for linear projects than other scheduling techniques. A new type of scheduling in linear project is calling Linear Scheduling Model (LSM). The Project monitoring and controlling is very ease to identify that all the stage of linear project and have more advantages.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.91-101
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• 2000

We propose a new method for estimating the mean of a population which has a linear trend. The suggested estimator is based on the centered balanced systematic sampling method and the concept of interpolation and extrapolation. The efficiency of the proposed method is compared with that of existing methods.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.175-185
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• 2002

A method is proposed for efficiently estimating the mean of a population which has a linear trend. The proposed estimator is based on the centered modified systematic sampling method and the concept of interpolation. Using the expected mean square error criterion, it is shown that the proposed method is more efficient than conventional methods in most real cases.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.91-102
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• 1999

A new method is developed for estimating the mean of a population which has a linear trend. The suggested estimator is based on the balanced systematic sampling method and the concept of interpolation and extrapolation. The efficiency of the proposed method is compared with that of conventional methods.

• 한국데이터정보과학회:학술대회논문집
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• 2001.10a
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• pp.17-24
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• 2001

A method is proposed for efficiently estimating the mean of a population which has a linear trend. The proposed estimator is based on the centered modified systematic sampling method and the concept or interpolation. Using the expected mean square error criterion, it is shown that the proposed method is more efficient than conventional methods in most real cases.

• 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.

• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.6
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• pp.1-6
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• 2008

This work proposes a fuzzy trend monitoring method for the fault detection of a gas turbine engine through analyzing measured performance data trend. The proposed trend monitoring technique can diagnose the engine status by monitoring major engine measured parameters such as fuel flow rate, exhaust gas temperature, rotor rotational speed and vibration, and then analyzing their time deppendent changes. In order to perform this, firstly the measured engine performance data variation is formulated using Linear Regression, and then faults are isolated and identified using fuzzy logic.

• Journal of Soil and Groundwater Environment
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• v.21 no.6
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• pp.67-79
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• 2016

Gaussian process regression (GPR) is proposed as a tool of long-term groundwater quality predictions. The major advantage of GPR is that both prediction and the prediction related uncertainty are provided simultaneously. To demonstrate the applicability of the proposed tool, GPR and a conventional non-parametric trend analysis tool are comparatively applied to synthetic examples. From the application, it has been found that GPR shows better performance compared to the conventional method, especially when the groundwater quality data shows typical non-linear trend. The GPR model is further employed to the long-term groundwater quality predictions based on the data from two domestically operated groundwater monitoring stations. From the applications, it has been shown that the model can make reasonable predictions for the majority of the linear trend cases with a few exceptions of severely non-Gaussian data. Furthermore, for the data shows non-linear trend, GPR with mean of second order equation is successfully applied.