• Asian Journal of Turfgrass Science
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• v.1 no.1
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• pp.18-29
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• 1987

This study was carried out to examine the effect of nitrogen application and clipping interval on the characteristics of several turf components of korean lawngrass for the basic data of lawn management. It was treated by Split plot design with three replications. The main plots were nitrogen levels with 0, 350, and 700kgN / ha, and the sub plots were clipping intervals with 10, 20, and 30 days The results obtained are summarized as follows ; 1. Increasing the rate of nitrogen fertilizer and frequent clipping increased tiller number of korean lawngrass and the maximum number of tillers obtained in October were recorded from 700kgN application and clipping treatment of 10 days interval. Meanwhile, treatment of 350kgN with 10 days clipping interval increased tillers much more than those of 700kgN with 20 and 30 days clipping intervals. 2. The average number of green leaves occurred during the growth period maximized by applying 700 kgN and clipping 10 days interval. 3. Increasing tiller numbers significantly decreased tops DM weight per tiller by clippng plants at interval of 10 and 20 days, irrespective of nitrogen applied, and with nil N, at the interval of 30 days. By applying 700kgN however, tops DM weight per tiller increased as the number of tillers increased consistently. 4. The highest tops DM weight was achieved from late August to early September by applying 350 and 700kgN. 5. During the growth period, nitrogen application increased unders(stolon+root) DM weight, and, at the same level of nitrogen applied, the increase in stolon DM weight enhanced by lengthening the clipping interval to 30 days. 6. Nitrogen efficiency to green leaves, stolon nodes and DM weight of root with high nitrogen was achieved as clipping interval was shortened.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.2
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• pp.247-268
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• 2011

Due to the application-specific nature of wireless sensor networks, the sensitivity to such a requirement as data reporting interval varies according to the type of application. Such considerations require an application-specific, parameter tuning paradigm allowing us to maximize energy conservation prolonging the operational network lifetime. In this paper, we propose a reporting interval adaptive, sensor control platform for energy-saving data gathering in wireless sensor networks. The ultimate goal is to extend the network lifetime by providing sensors with high adaptability to application-dependent or time-varying, reporting interval requirements. The proposed sensor control platform is based upon a two phase clustering (TPC) scheme which constructs two types of links within each cluster - namely, direct link and relay link. The direct links are used for control and time-critical, sensed data forwarding while the relay links are used only for multi-hop data reporting. Sensors opportunistically use the energy-saving relay link depending on the user reporting, interval constraint. We present factors that should be considered in deciding the total number of relay links and how sensors are scheduled for sensed data forwarding within a cluster for a given reporting interval and link quality. Simulation and implementation studies demonstrate that the proposed sensor control platform can help individual sensors save a significant amount of energy in reporting data, particularly in dense sensor networks. Such saving can be realized by the adaptability of the sensor to the reporting interval requirements.

• Journal of applied mathematics & informatics
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• v.20 no.1_2
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• pp.549-556
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• 2006

In this paper, we consider interval-valued Choquet integrals and fuzzy measures. Using these properties, we discuss some applications of them in multicriteria decision aid. In particular, we show how these interval-valued Choquet integrals can model behavioral analysis of aggregation in ulticriteria decision aid.

• Journal of Korean Society for Quality Management
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• v.34 no.1
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• pp.54-72
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• 2006

In recent years, the widespread application of quality management and measurement reliability has put increasing emphasis on procedures for periodic instrument calibration. By optimizing calibration intervals, unnecessary calibrations can be minimized, thereby reducing costs. Moreover, optimizing intervals will improve compliance with regulatory directives while ensuring maximal compliance with reliability targets. In this paper, we present Calibration Interval Analysis Program developed using several establishment methodologies of calibration interval for measurement quality and reliability improvement. Also, we perform calibration interval analysis for some measurement instruments and analyse its results.

• Journal of Medicine and Life Science
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• v.20 no.3
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• pp.107-114
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• 2023

Epidemiological control of coronavirus disease 2019 (COVID-19) is needed to estimate the infection period of confirmed cases and identify potential cases. The present study, targeting confirmed cases for which the time of COVID-19 symptom onset was disclosed, aimed to investigate the relationship between intervals (day) from symptom onset to testing the cycle threshold (CT) values of real-time reverse transcription-polymerase chain reaction. Of the COVID-19 confirmed cases, those for which the date of suspected symptom onset in the epidemiological investigation was specifically disclosed were included in this study. Interval was defined as the number of days from symptom onset (as disclosed by the patient) to specimen collection for testing. A locally weighted regression smoothing (LOWESS) curve was applied, with intervals as explanatory variables and CT values (CTR for RdRp gene and CTE for E gene) as outcome variables. After finding its non-linear relationship, a polynomial regression model was applied to estimate the 95% confidence interval values of CTR and CTE by interval. The application of LOWESS in 331 patients identified a U-shaped curve relationship between the CTR and CTE values according to the number of interval days, and both CTR and CTE satisfied the quadratic model for interval days. Active application of these results to epidemiological investigations would minimize the chance of failing to identify individuals who are in contact with COVID-19 confirmed cases, thereby reducing the potential transmission of the virus to local communities.

• Structural Engineering and Mechanics
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• v.62 no.6
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• pp.703-708
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• 2017

The goal of this study is to evaluate behavior uncertainties of structures by using interval finite element analysis based on interval factor method as a specific non-stochastic tool. The interval finite element method, i.e., interval FEM, is a finite element method that uses interval parameters in situations where it is not possible to get reliable probabilistic characteristics of the structure. The present method solves the uncertainty problems of a 2D solid structure, in which structural characteristics are assumed to be represented as interval parameters. An interval analysis method using interval factors is applied to obtain the solution. Numerical applications verify the intuitive effectiveness of the present method to investigate structural uncertainties such as displacement and stress without the application of probability theory.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.2119-2132
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• 1996

This paper proposes four approaches for approximately realizing nonlinear mappling of interval vectors by neural networks. In the proposed approaches, training data for the learning of neural networks are the paris of interval input vectors and interval target output vectors. The first approach is a direct application of the standard BP (Back-Propagation) algorithm with a pre-processed training data. The second approach is an application of the two BP algorithms. The third approach is an extension of the BP algorithm to the case of interval input-output data. The last approach is an extension of the third approach to neural network with interval weights and interval biases. These approaches are compared with one another by computer simulations.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.105-117
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• 2003

The purpose of this research aimed at performing the easy design. and also the easy on-the-job application or the maintenance interval determination methodology by presenting the determining model or the optimizing maintenance interval in TBM for the preventive maintenance or facility TBM(time-based maintenance) as the preventive maintenance requires the adequate determination or the maintenance interval. The maintenance interval or TBM shall be applied differently for the each interval such as He patrol inspection, maintenance, overhaul inspection. exchange. And it is based on the composition level of equipment. The already informed theories or interval determination methodology for the patrol inspection. repair. and overhaul inspection are difficult for adopting because or the several restriction problems in applying the maintenance schemes as the theory So, the model for determining the optimizing exchange interval or part, maintenance interval of auxiliary machine, unit equipment etc. was presented to apply in the maintenance easily and appropriately.

• Journal of Applied and Pure Mathematics
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• v.6 no.3_4
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• pp.105-117
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• 2024

In the classical sense, it is known that it is impossible to construct a vector space over the entire set of real numbers with the help of simple interval arithmetic. In this article, it has shown that a vector space can be constructed in the classical sense by helping Markov's extended interval arithmetic on the interval valued Cesaro sequence spaces of non-absolute type. As a result of the positive answers, this idea was extended by us with some theorems. Consequently, a new perspective was gained to the construction of new types of sequence spaces by using different algebraic operations on interval-valued sequence spaces.

• International Journal of Reliability and Applications
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• v.3 no.1
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• pp.37-50
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• 2002

Simultaneous estimation of accuracy and probability corresponding to a prediction interval is considered in this study. Traditional application of confidence interval forecasting consists in evaluation of interval limits for a given significance level. The wider is this interval, the higher is probability and the lower is the forecast precision. In this paper a measure of stochastic forecast accuracy is introduced, and a procedure for balanced estimation of both the predicting accuracy and confidence probability is elaborated. Solution can be obtained in an optimizing approach. Suggested method is applied to constructing confidence intervals for parameters estimated by normal and t distributions