Safe Learning Reference Governor: Theory and Application to Fuel Truck Rollover Avoidance

This paper proposes a learning reference governor (LRG) approach to enforce state and control constraints in systems for which an accurate model is unavailable; and this approach enables the reference governor to gradually improve command tracking performance through learning while enforcing the constraints during learning and after learning is completed. The learning can be performed either on a black-box type model of the system or directly on the hardware. After introducing the LRG algorithm and outlining its theoretical properties, this paper investigates LRG application to fuel truck (tank truck) rollover avoidance. Through simulations based on a fuel truck model that accounts for liquid fuel sloshing effects, we show that the proposed LRG can effectively protect fuel trucks from rollover accidents under various operating conditions.

Self-adapting model for variable stiffness magnetorheological dampers

Variable stiffness magnetorheological fluid (MRF) dampers inherently have special nonlinear characteristics and complex structures. An accurate model describing the nonlinearity is the key for the damper to operate under variable conditions. This paper proposes a self-adapting model to characterize the variable stiffness MRF dampers through corresponding optimized algorithm. The experimental results verify the capability of the self-adapting of the model parameters. The model can describe the nonlinear characteristics of the variable stiffness MRF damper when conditions are changed. The proposed self-adaptive model improves the model accuracy which provide an approach for modeling complex dampers under variable working conditions.

New End-Stopped Complex Cell Model Applicable in Convolutional Neural Networks

This article is dedicated to modeling the end-stopped neuron. This type of neuron gives the maximum response at the end of the line and is used to refine the edge. The article provides an overview of different models of end-stopped neurons. I have proposed a simpler and more accurate model of an end-stopped neuron based on the use of Gabor filters in antiphase. For this purpose, the models of simple and complex cells whose output is used in the proposed model are also described. Simple cells are based on the use of a Gabor filter, the parameters of which are also described in this article. The proposed model has shown its effectiveness.

Effect of Climate Change on the Rainfall Trends: A case study of Pune, India

It is the need of the hour to predict the impact of climate change, especially rainfall on the future environmental conditions on local as well as global scales. The present work aims at studying the impact of climate change on the rainfall occurring over Pune, the eighth largest city in India. The General Circulation Models (GCMs) are predominantly used to obtain the climate data all over the globe, at various grid points, for past and future years. Rainfall values obtained from these grid points need to be downscaled to make them location specific. This study proposes a soft computing tool, Artificial Neural Network (ANN) for the purpose of downscaling. The rainfall data at 4 grid points surrounding Pune, was extracted from 5 different GCMs and given as input to ANN with observed rainfall as output, thus forming 5 models. For comparison, a pre-existing downscaling technique, Distribution based scaling (DBS) was used. The coefficient of correlation (r) showed that ANN was working better than DBS. The value of r for ANN was 0.73 for its least accurate model whereas DBS managed to reach 0.73 for its most accurate model. The future rainfall estimated with the help of the trained ANN models show an increase in mean rainfall over the Pune region by ∼2 – 15% and decrease in maximum rainfall by ∼40 – 65%. Peak prediction of rainfall simulated by ANN was not very accurate and hence there is still an opportunity for improvement which is the future scope of this study.

A modified model of conductor roughness for manufacturing copper lines of printedcircuit board

Purpose The purpose of this paper is to establish an accurate model to quantify the effect of conductor roughness on insertion loss (IL) and provide improved measurements and suggestions for manufacturing good conductive copper lines of printed circuit board. Design/methodology/approach To practically investigates the modified model of conductor roughness, three different kinds of alternate oxidation treatments were used to provide transmission lines with different roughness. The IL results were measured by a vector net analyzer for comparisons with the modified model results. Findings An accurate model, with only a 1.8% deviation on average from the measured values, is established. Compared with other models, the modified model is more reliable in industrial manufacturing. Originality/value This paper introduces the influence of tiny roughness structures on IL. Besides, this paper discusses the effect of current distribution on IL.