A Mathematical Model for Cell Zooming Mechanism of Base Station using Classification Approach

In this paper, an efficient approach for cell zooming mechanism of Base Station is proposed using classification method. The proposed cell zooming methodology has the following stages: capacity computation of base station, determination of payload of each cell and processing of BS to be switched ON/OFF using Neural Network classification approach. The performance of the proposed cell zooming methodology using classification approach is analyzed in terms of call handling rate, latency and throughput. The proposed cell zooming mechanism using feed forward back propagation neural networks achieves 94.2\% of CHR, 14.4 ns of latency and 18,187 bits/esc of throughput

A Numerical Simulation on MHD Mixed Convection in a Lid-driven Cavity with Corner Heaters

A numerical investigation on mixed convection in a lid-driven square cavity has been performed in the presence of the uniform magnetic field. From the left-bottom corner of the cavity, three different lengths of heater are varied along bottom and left walls simultaneously. The finite volume method is employed to solve the governing equations. It is observed that the heater length in the x-direction is more effective than that of in the y-direction on the heat transfer and on the flow pattern. The magnetic field affects the average heat transfer rate more on vertical heaters than on the horizontal heaters

Effects of variable viscosity on natural convection flow of an optically thick gray gas past a horizontal surface in the presence of internal heat generation

A numerical investigation to discuss the effects of radiation and variable viscosity on heat and mass transfer characteristics of natural convection over a horizontal surface embedded in a saturated porous medium in the presence of internal heat generation is carried out in this study. The working fluid for the investigation is optically thick gray gas. The Dufour and Soret effects are also taken into account. Similarity transformations are employed to obtain nonlinear ordinary differential equations from the governing equations of the present problem. The numerical results for the transformed governing equations are computed by using commercial boundary value problem solver for ordinary differential equations. The effects are discussed by varying the parameters such as radiation, Dufour and Soret numbers, buoyancy ratio, Prandtl number, Schmidt number, and variable viscosity. Presence of internal heat generation enhances the velocity profile and significantly decreases the concentration boundary layer thickness. On increasing fluid radiation, the temperature of the fluid is higher than that of the surface and the concentration boundary layer thickness decreases away from the surface

An Investigation of\ua0Challenges Encountered When Specifying Training Data and\ua0Runtime Monitors for\ua0Safety Critical ML Applications

[Context and motivation] The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the development of ML models have major influences on the later behaviour of the system. Runtime monitors are used to provide guarantees for that behaviour. [Question/problem] We see major uncertainty in how to specify training data and runtime monitoring for critical ML models and by this specifying the final functionality of the system. In this interview-based study we investigate the underlying challenges for these difficulties. [Principal ideas/results] Based on ten interviews with practitioners who develop ML models for critical applications in the automotive and telecommunication sector, we identified 17 underlying challenges in 6 challenge groups that relate to the challenge of specifying training data and runtime monitoring. [Contribution] The article provides a list of the identified underlying challenges related to the difficulties practitioners experience when specifying training data and runtime monitoring for ML models. Furthermore, interconnection between the challenges were found and based on these connections recommendation proposed to overcome the root causes for the challenges