• Journal of Institute of Control, Robotics and Systems
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• v.16 no.2
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• pp.177-187
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• 2010

A MTG (Multimode Tracking and Guidance) system is employed to compensate for the limitations of individual seekers such as RF (Radio frequency) or IIR (Imaging Infra-red) and to improve the overall tracking and guidance performance in jamming, clutter, and adverse weather environments. In the MTG system, tracking filter, data association, and data fusion methods are important elements to maximize the effectiveness of precision homing missile guidance. This paper proposes the formulation of a Kalman filter for the estimation of line-of-sight rate from seeker measurements in missiles guided by proportional navigation. Also, we suggest the HPDA (Highest Probability Data Association) and data fusion methods of the MTG system for target tracking in the adverse environments. Mont-Carlo simulation is employed to evaluate the overall tracking performance and guidance accuracy.

• International Journal of Reliability and Applications
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• v.9 no.2
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• pp.125-140
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• 2008

The new better than used failure rate (NBUFR), Abouammoh and Ahmed (1988), and new better than average failure rate (NBAFR) Loh (1984) classes of life distributions, have been considered in the literature as natural weakenings of NBU (NWU) property. The paper considers testing exponentiality against strictly NBUFR (NBAFR) alternatives, or their duals, based on goodness of fit approach that is possible in life testing problems and that it results in simpler procedures that are asymptotically equivalent or better than standard ones. They may also have superior finite sample behavior. The asymptotic normality are proved. Powers, Pitman asymptotic efficiency and critical points are computed. Dealing with censored data case also studied. Practical applications of our tests in the medical sciences are present.

• Earthquakes and Structures
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• v.16 no.5
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• pp.523-532
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• 2019

Different types of gas reservoir such as Liquid Natural Gas (LNG) are among the strategic infrastructures, and have great importance for any government or their private owners. To keep the tank and its contents safe during earthquakes especially if the contents are of hazardous or flammable materials; using seismic protection systems such as base isolator can be considered as an effective solution. However, the major deficiency of this system can be the large deformation in the isolation level which may lead to the failure of bearing system. In this paper, as a solution, the efficacy of an optimally designed combined vibration control system, the combined laminated rubber isolator and rotational friction damper, is investigated to evaluate the enhancement of an existing metal tank response under both far- and near-field earthquakes. Responses like impulsive and convective accelerations, base shear, and sloshing height are studied herein. The probabilistic framework is used to consider the uncertainties in the structural modeling, as well as record-to-record variability. Due to the high calculation cost of probabilistic methods, a simplified structural model is used. By using the Mont-Carlo simulation approach, it is revealed that this combined isolation system is a highly reliable system which provides considerable enhancement in the performance of reservoir, not only leads to the reduction of probability of catastrophic failure of the tank but also decrease the reservoir damage during the earthquake. Moreover, the relative displacement of the isolation level is controlled very well by this combined system.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.33-43
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• 2002

Various kinds of estimation methods have been developed for imputation of categorical missing data. They include category method, logistic regression, and association rule. In this study, we propose two fusions algorithms based on both neural network and voting scheme that combine the results of individual imputation methods. A Mont-Carlo simulation is used to compare the performance of these methods. Five factors used to simulate the missing data pattern are (1) input-output function, (2) data size, (3) noise of input-output function (4) proportion of missing data, and (5) pattern of missing data. Experimental study results indicate the following: when the data size is small and missing data proportion is large, modal category method, association rule, and neural network based fusion have better performances than the other methods. However, when the data size is small and correlation between input and missing output is strong, logistic regression and neural network barred fusion algorithm appear better than the others. When data size is large with low missing data proportion, a large noise, and strong correlation between input and missing output, neural networks based fusion algorithm turns out to be the best choice.