• Journal of Astronomy and Space Sciences
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• v.32 no.4
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• pp.367-377
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• 2015

Atmospheric drag force is an important source of perturbation of Low Earth Orbit (LEO) orbit satellites, and solar activity is a major factor for changes in atmospheric density. In particular, the orbital lifetime of a satellite varies with changes in solar activity, so care must be taken in predicting the remaining orbital lifetime during preparation for post-mission disposal. In this paper, the System Tool Kit (STK$^{(R)}$) Long-term Orbit Propagator is used to analyze the changes in orbital lifetime predictions with respect to solar activity. In addition, the STK$^{(R)}$ Lifetime tool is used to analyze the change in orbital lifetime with respect to solar flux data generation, which is needed for the orbital lifetime calculation, and its control on the drag coefficient control. Analysis showed that the application of the most recent solar flux file within the Lifetime tool gives a predicted trend that is closest to the actual orbit. We also examine the effect of the drag coefficient, by performing a comparative analysis between varying and constant coefficients in terms of solar activity intensities.

• Journal of Computing Science and Engineering
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• v.4 no.2
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• pp.128-152
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• 2010

With the advance of computer and communication technologies, wireless sensor networks (WSNs) are increasingly used in many aspects of our daily life. However, since the battery lifetime of WSN nodes is restricted, the WSN lifetime is also limited. Therefore, it is crucial to determine this limited lifetime in advance for preventing service interruptions in critical applications. This paper proposes a feasible static analysis approach to estimating the worstcase lifetime of a WSN. Assuming known routes with a given sensor network topology and SMAC as the underlying MAC protocol, we statically estimate the lifetime of each sensor node with a fixed initial energy budget. These estimations are then compared with the results obtained through simulation which run with the same energy budget on each node. Experimental results of our research on TinyOS applications indicate that our approach can safely and accurately estimate worst-case lifetime of the WSN. To the best of our knowledge, our work is the first one to estimate the worst-case lifetime of WSNs through a static analysis method.

• Advances in aircraft and spacecraft science
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• v.3 no.4
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• pp.503-521
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• 2016

Variances in turbomachinery blades caused by manufacturing, operation or regeneration can result in modified structural behavior. In this work, the scatter of geometrical and material properties of a turbine blade and its influence on structure performance is discussed. In particular, the vibration characteristics and the lifetime of a turbine blade are evaluated. Geometrical variances of the surface of the blades are described using the principal component analysis. The scatter in material properties is considered by 16 varying material parameters. Maximum vibration amplitudes and the number of load cycles the turbine blade can withstand are analyzed by finite element simulations incorporating probabilistic principles. The probabilistic simulations demonstrate that both geometrical and material variances have a significant influence on the scatter of vibration amplitude and lifetime. Dependencies are quantified and correlations between varied input parameters and the structural performance of the blade are detected.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.05a
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• pp.202-204
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• 2008

This paper gives a comparative analysis on lifetime of the fluorescent induction lamp. We have treasured and analysed optical characteristics of circular type fluorescent induction lamps, which has A and B types, to estimate lifetime. In the result of analysis on lifetime, $B_{10}$ lifetime of A and B type lamps are 466 hours and 1,595 hours, respectively.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.04a
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• pp.296-297
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• 2011

A series of thermogravimetric analysis tests were conducted to predict the lifetime of the PTFE electrical insulation material. The prepared PTFE samples were heated from $25^{\circ}C$ to $700^{\circ}C$ at different heating rates. The kinetic energy of the PTFE was calculated from the logarithmic heating rate versus reciprocal temperature curves at constant conversion levels. Also, the lifetime of the PTFE for a given operating temperature can be predicted using the relationship between the activation energy and the estimated lifetime proposed by Toop.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2302-2324
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• 2014

Cluster-based wireless sensor network (WSN) can significantly reduce the energy consumption by data aggregation and has been widely used in WSN applications. However, due to the intrinsic many-to-one traffic pattern in WSN, the network lifetime is generally deteriorated by the unbalanced energy consumption in a cluster-based WSN. Therefore, energy efficiency and network lifetime improvement are two crucial and challenging issues in cluster-based WSNs. In this paper, we propose a Non-Uniform Node Distribution (NUND) scheme to improve the energy efficiency and network lifetime in cluster-based WSNs. Specifically, we first propose an analytic model to analyze the energy consumption and the network lifetime of the cluster-based WSNs. Based on the analysis results, we propose a node distribution algorithm to maximize the network lifetime with a fixed number of sensor nodes in cluster-based WSNs. Extensive simulations demonstrate that the theoretical analysis results determined by the proposed analytic model are consistent with the simulation results, and the NUND can significantly improve the energy efficiency and network lifetime.

• Journal of Korean Society for Quality Management
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• v.38 no.4
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• pp.504-511
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• 2010

A fuze detonator comprising star shells is an important device so that its failure usually leads to failure of the shells. In this paper, accelerated degradation tests of RD1333 (lead azide) using temperature stress were performed, and then degradation data of explosive power for the detonator were analyzed to predict the storage lifetime of detonator. Degradation data analysis to estimate the storage lifetime is based on a distribution-based degradation process. Statistical distribution parameters of explosive power degradation measures at each time were estimated for each temperature level, and then reliability of the detonator for each accelerated temperature level was estimated using both time-varying distribution parameters and critical level of explosive power. Arrhenius model was applied to estimate storage lifetime of the detonator under the field temperature condition. Accelerated distribution-based degradation analysis to estimate storage lifetime is explained in detail, and estimation results are compared to field data of storage lifetime in this paper.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.687-696
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• 2011

Customer Lifetime or Customer Lifetime Value is a essential metric of differentiated CRM marketing and differentiated marketing strategy as a company core competency. However, customer lifetime used in companies is easily obtained from a confined simple customer attrition rate at some specific time point regardless of customer characteristics. In this study, in order to overcome the constraints of previous simple methods and to make practical use of it in industries, we suggest a method that estimates a customer lifetime using a customer segment based survival analysis with the censored data of customers; in addition, we apply this method to A mobile telecom company data. A method using customer segment based survival analysis is suggested in this study 1) includes all customers having different subscription dates, 2) reduces individual error, 3) can reflect trends after the observed time point and is more realistic.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.11
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• pp.1605-1611
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• 2017

In this study, the characteristic lifetime of power facilities such as power transformers and GIS were investigated as the basis of power facility asset management. It is difficult to obtain the operation and failure data of the facilities in Korea. Therefore, the number of failures of the electric power facilities was calculated from the operation data and hazard rate shown in the overseas literatures, and the statistical analysis was performed using the Weibull distribution function. As a result of extracting and analyzing the data of the UK National Grid for power transformers, the characteristic lifetime (scale parameter) of 116.45 years was considered to be a very appropriate value for power transformer management and can be used as a comparative data of the analysis of the domestic transformers. As for the GIS, based on the Bays and Bay-Years data and the hazard rate according to the operation years of the 123kV GIS in Germany, it is found out that the characteristic lifetime of GIS is not so meaningful. It is necessary to decide a maintenance strategy and lifetime expectancy considering the characteristics of the design, materials and manufacturing process of GIS.

• Asian Journal of Innovation and Policy
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• v.1 no.2
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• pp.200-218
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• 2012

Estimating the economic life of a technology is the first important prerequisite step in the feasibility analysis of technology-based business. Many empirical studies have concentrated on patents data to estimate the time period for a technology. However, it is recommended to estimate it along with qualitative considerations of future technological and market conditions. In this regard, little is known about how approaches are applied. This paper aims to establish a structural framework of estimating the lifetime of a technology by integrating the outputs of an analysis of the determinants in each transition of a product life cycle. We describe an illustrative case about a light emitting diode (LED) backlight unit (BLU) technology for the liquid crystal display (LCD) TV. The framework allows valuators and experts to estimate a technology lifetime by using multidimensional factors.