• East Asian Economic Review
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• v.27 no.4
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• pp.273-301
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• 2023

This study investigates the impacts of long-term climate variability on household consumption in Indonesia, a country highly vulnerable to climate change. The analysis combines household survey data from nearly 5,998 families with satellite-derived weather data from NASA POWER spanning 30 years. We use the long-term variability in temperature and precipitation as a proxy for climate change. This study examines the impact of climate change which proceeds over the long term, unlike previous studies concerning one-off or short-term climate events. In addition, using satellite data enhances the accuracy of households' exposure to climate variability. The analysis finds that households in a village with higher temperature and precipitation variability significantly consume less food. This implies that households more exposed to climate change are at higher risk of malnutrition in developing countries. This study has a limitation that it cannot rule out the potential endogeneity of choosing a climate-vulnerable residential location due to economic poorness.

• Smart Structures and Systems
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• v.3 no.3
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• pp.341-356
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• 2007

A good understanding of normal modal variability of civil structures due to varying environmental conditions such as temperature and wind is important for reliable performance of vibration-based damage detection methods. This paper addresses the quantification of wind-induced modal variability of a cable-stayed bridge making use of one-year monitoring data. In order to discriminate the wind-induced modal variability from the temperature-induced modal variability, the one-year monitoring data are divided into two sets: the first set includes the data obtained under weak wind conditions (hourly-average wind speed less than 2 m/s) during all four seasons, and the second set includes the data obtained under both weak and strong (typhoon) wind conditions during the summer only. The measured modal frequencies and temperatures of the bridge obtained from the first set of data are used to formulate temperature-frequency correlation models by means of artificial neural network technique. Before the second set of data is utilized to quantify the wind-induced modal variability, the effect of temperature on the measured modal frequencies is first eliminated by normalizing these modal frequencies to a reference temperature with the use of the temperature-frequency correlation models. Then the wind-induced modal variability is quantitatively evaluated by correlating the normalized modal frequencies for each mode with the wind speed measurement data. It is revealed that in contrast to the dependence of modal frequencies on temperature, there is no explicit correlation between the modal frequencies and wind intensity. For most of the measured modes, the modal frequencies exhibit a slightly increasing trend with the increase of wind speed in statistical sense. The relative variation of the modal frequencies arising from wind effect (with the maximum hourly-average wind speed up to 17.6 m/s) is estimated to range from 1.61% to 7.87% for the measured 8 modes of the bridge, being notably less than the modal variability caused by temperature effect.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.27-30
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• 1996

PR interval variability has been proposed as a noninvasive tool for in-vestigating the autonomic nervous system as welt as heart rate variability. The goal of this paper is to determine whether PR interval variability is generated from deterministic nonlinear dynamics. The data used in this study is a 24-hour bolter ECGs of 20 healthy adults. We developed an automatic PR interval measurement algorithm, and tested it using MIT ECG Databases. The general discriminants of nonlinear dynamics, such as, correlation dimension and phase space reconstruction are used. Surrogate data is generated from simpler linear models to have similar statistical characteristics with the original data. Nonlinear discriminants are applied to both data, and compared for any significant results. It was concluded that PR interval variability shows non-linear characteristics.

• Korean Journal of Applied Biomechanics
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• v.24 no.4
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• pp.359-365
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• 2014

A study was conducted to investigate the possible effects of fatigue which was resulted from increased running time on the stability during a prolonged run. The purposes of this study were twofold: first, to determine the discrete and non-linear variability of GRF (ground reaction force) components between running times to know the body stability, and second, to determine the pattern between discrete and non-linear variability. Nineteens healthy young adult males served in this study as subjects who ran at their preferred running speed. GRF data for twenty strides were collected at 5, 65, and 125 minutes during run. Variance coefficient and Lyapunov Exponent techniques on the GRF data were used to calculate variability index for each of the running time conditions. There were no difference between discrete variabilities of three components of GRF, but non-linear variability of the Fz component of GRF was decreased by increasing running time (p<.01). No relationship was found between discrete and non-linear variability.

• Journal of Biosystems Engineering
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• v.33 no.5
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• pp.355-361
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• 2008

As agricultural machinery has become larger and tillage practices have changed in recent decades, compaction as a result of wheel traffic and tillage has caused increasing concern. If strategies to manage compaction, such as deep tillage, could be applied only where needed, economic and environmental benefits would result. For such site-specific compaction management to occur, compacted areas within fields must be efficiently sensed and mapped. We previously developed an on-the-go soil strength profile sensor (SSPS) for this purpose. The SSPS measures within-field variability in soil strength at five soil depths up to 50 cm. Determining the variability structure of SSPS data is needed for site-specific field management since the variability structure determines the required intensity of data collection and is related to the delineation of compaction management zones. In this paper, soil bin data were analyzed by a spectral analysis technique to determine the variability structure of the SSPS data, and to investigate causes and implications of this variability. In the soil bin, we observed a repeating pattern due to soil fracture with an approximate 12- to 19-cm period, especially at the 10-cm depth, possibly due to cyclic development of soil fracture on this interval. These findings will facilitate interpretation of soil strength data and enhance application of the SSPS.

• Journal of Environmental Science International
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• v.22 no.10
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• pp.1305-1316
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• 2013

A long-term wind resource map was made to provide the key design data for the 2.5 GW Korean West-South Offshore Wind Project, and its reliability was validated. A one-way dynamic downscaling of the MERRA reanalysis meteorological data of the Yeongwang-Gochang offshore was carried out using WindSim, a Computational Fluid Dynamics based wind resource mapping software, to establish a 33-year time series wind resource map of 100 m x 100 m spatial resolution and 1-hour interval temporal resolution from 1979 to 2012. The simulated wind resource map was validated by comparison with wind measurement data from the HeMOSU offshore meteorological tower, the Wangdeungdo Island meteorological tower, and the Gochang transmission tower on the nearby coastline, and the uncertainty due to long-term variability was analyzed. The long-term variability of the wind power was investigated in inter-annual, monthly, and daily units while the short-term variability was examined as the pattern of the coefficient of variation in hourly units. The results showed that the inter-annual variability had a maximum wind index variance of 22.3% while the short-term variability, i.e., the annual standard deviation of the hourly average wind power, was $0.041{\pm}0.001$, indicating steady variability.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.434-436
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• 2005

Analysis method using remote sensing data is one of the effective ways to research a spatial and temporal variability of the mesoscale oceanic motions. During past several decades, many researchers have been getting comprehensive results using remote sensing data with application of data fusion methods in many parts of geo-science. For this study, we took the integration and fusion of several remote sensing data, which are different data resolution, timescale and characteristics, for improving accurate analysis of variation of the Kuroshio Extension. Furthermore, we might get advanced ways to understand the variability of the Kuroshio Extension, has close relation to the spatial and temporal variation of the Kuroshio and Oyashio Current.

• Proceedings of the KSRS Conference
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• v.2
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• pp.922-925
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• 2006

The objective of this study is first to resolve the spatial and seasonal variability of BL in the bay using 'the most comprehensive' data set available for the bay and then to understand the formation mechanisms and variability in the light of the known dynamical and thermodynamical processes. The most recent study [Masson et al., 2002] on the BL variability in the bay was based on the World Ocean Atlas (WOA98) of Levitus [1998]. The temperature and salinity profiles in the bay have increased considerably after the release of WOA98. The WOA98, itself has been updated to WOA01 in 2001. Further, the deployment of ARGO profiling floats in the bay since 2002 has generated many additional profiles. In addition to the ARGO data and the updated WOA01, the hydrographic data collected from the bay under several Indian national programs and archived in the Indian Oceanographic Data Centre (IODC) was also considered in the present study. The WOA98 and WOA01 consist of only limited data from the IODC archive, especially from the Exclusive Economic Zone of India. Therefore, the combination of these data from the three different sources (WOA01, ARGO and IODC) provides ‘the most comprehensive data set’ for the bay to resolve the BLT structure and its variability in a much better scale than in the past.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.991-997
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• 2006

Recently, Liu (2005) proposed a special type of weighting function under a given preference index level with the minimal variability similar to the minimal variability OWA operator weights problem proposed by Fuller and Majlender (2003). He solved this problem using a result of classical optimal control theory. In this note, we give a direct elementary proof of this problem without using any known results.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.11
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• pp.739-746
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• 2013

Variability of the software product line that differentiates member products within a product line must be described with precise meaning and visualized so as easy to select. Moreover, it should be easy to manage. Variability description approaches can largely be divided into two approaches, integrated variability description approach and orthogonal variability description approach. Orthogonal Variability Description Language (OVDL) was developed for clear and precise description of variability without ambiguity. This paper validates the variability description capability of OVDL by translating the variability models of Inter-Working Function (IWF) product line described by using Orthogonal Variability Model (OVM) notations into variability descriptions in OVDL.