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Statistical Estimation and Mathematical Modelling of Tropospheric Radio Refractivity Based on Meteorological Data

Alimgeer K. S., Awais M., Ijaz B., Khan S. A., Subhan F., Ahmad N.

Metrology and Measurement Systems

2018

Vol. 25, nr 1

115--128

To obtain optimal performance, estimation of radio refractivity is essential in planning and design of radio links/systems. The dependence of radio refractivity on different climatic parameters such as temperature, pressure, humidity and scale height is studied. The method proposed by International Telecommunication Union (ITU) is used in calculation of tropospheric radio refractivity. The main objective of this work is to formulate a mathematical equation/expression which can be used to predict/estimate the value of radio refractivity for an arbitrary day of any year, with a low error of estimation, in a local environment. Sensitivity of radio refractivity to temperature, pressure and humidity has been evaluated for a period of 5 years from 2008 to 2012, in a particular area under consideration. The results presented for tropospheric radio refractivity take into consideration both the location height and scale height parameters. The results are analysed in terms of statistical measures such as the moving averages, probability density function, monthly mean values, and their corresponding standard deviations. Finally, a mathematical model is formulated to calculate the radio refractivity for any day of a whole year. Reliability of error analysis in respect to accuracy is also shown. It was implemented in the industrial enterprise.

Seismic activity prediction using computational intelligence techniques in northern Pakistan

Asim K. M., Awais M., Martinez-Alvarez F., Iqbal T.

Acta Geophysica

2017

Vol. 65, no. 5

919--930

Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar’s statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.

Research activities of computer-aided materials processing laboratory

Im Y. T., Lee H. W., Lee H. C., Awais M., Jin Y. G., Jung K. H., Jung K. H.

Journal of Achievements in Materials and Manufacturing Engineering

2007

Vol. 24, nr 1

219-229

Purpose: of this paper is to review the research works carried out at the national research laboratory for computer-aided materials processing at the department of mechanical engineering at KAIST. Design/methodology/approach: The research papers published so far from the laboratory were carefully reviewed and highlights for developing simulation tools for mesh generation, 2D or 3D finite element analyses for forging, shape rolling, solidification, semi-solid forging, compression molding of thermoset composites, injection molding without or with short fibers, and expert system for multi-stage axi-symmetric cold forging, extrusion, and multi-pass shape rolling are recaptured. Findings: According to this survey, the important issues involved with program developments and their industrial applications were revisited. Research limitations/implications: Understanding of material behaviour at various processing conditions and characterization of proper boundary conditions in terms of friction and temperature should be carefully made. Handling of complex geometry and computational efficiency for such geometry should be improved as well. Further development of three dimensional design systems should be necessary. Practical implications: Proper usage of the simulation tools and interface such tools with the automatic design system with the help of artificial intelligence will be very useful at the design stage of new manufacturing products and processes. In addition, proper understanding of deformation mechanics is of importance to properly utilize such numerical tools. Originality/value: Various aspects of limitations involved with program developments and their usage are identified and some important industrial applications demonstrated.