Complex Dynamics of Meditating Body

Bhardwaj, Rashmi; Bangia, Aashima

doi:10.5958/1945-919x.2016.00010.4

Cited by 9 publications

(4 citation statements)

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“…Bettayeb and Mansouri [5] developed PID fractional-order filter controller design for integer-order systems. Bhardwaj and Bangia [6][7] studied the complexity of meditating body via mathematical modelling of differential equations. Also, developed a conjunction model using Neuro-fuzzy for analysis of NSE for the period of Demonetization.…”

Section: Electricmentioning

confidence: 99%

Hybrid Fuzzified-PID Controller for non-linear Control Surfaces for DC Motor to Improve the Efficiency of Electric Battery Driven Vehicles

Bhardwaj¹,

Bangia²

2019

IJRTE

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Abstract: The paper intends to deliver a structure of speed-control for electric DC motor widely being used in the electric rechargeable-battery vehicles. Electric vehicles are the need of the hour due to increasing environmental concerns and the dependency on fuels and oils. So as to promote this hybrid and electric vehicle technology and ensure its sustenance, the Ministry of Heavy Industry and Public Enterprises in the Gazette of India on 13th of March, 2015 approved the Scheme for Faster adoption and manufacture of (Hybrid &) Electric Vehicle in India referred as FAME-India under National Electric Mobility Mission (NEMM). This scheme intends to encourage the hybrid/electric motor driven vehicles in the market and also its manufacturing for the betterment of eco-system to be implemented over a period of six years till 2020. Electric-battery driven vehicle is sourced on the restricted electrical-energy delivered by the battery in circuit. Major contribution of this work is to propose control-strategy through Fuzzified-PID controller so that the performances of the electric vehicle is comparable to that of an internal combustion-engine vehicle. Feedback is the foundation of PID control. The target or the set point is compared with the resultant of the process. Then, correction is computed and applied for the difference identified. This procedure is carried on till the time recalculation is required. PID refers to the combined computation of proportional-integral-derivative. Controllers, in general do not apply all three mathematical functions. Maximum processes were being handled through the proportional-integral-terms. However, addition of derivative control for fine control plus to avoid overshoot are required. Following models: PID controller, hybrid Fuzzified-reasoning PID controllers for linear surfaces and non-linear control surfaces using n-D Lookup-Table data have been designed for a comparative study. It has been observed that hybrid model designed for non-linear control-surfaces provided better speed response and have zero steady state error. The simulation of these models is carried out using SIMULINK under varying state conditions.

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Section: Electricmentioning

confidence: 99%

Hybrid Fuzzified-PID Controller for non-linear Control Surfaces for DC Motor to Improve the Efficiency of Electric Battery Driven Vehicles

Bhardwaj¹,

Bangia²

2019

IJRTE

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“…Chloroquine drug usages, action mechanisms and other characteristics from various diseases were studied for this pandemic treatment [1] . Estimating atmosphere pollutants through dynamic indicators, discussion of the meditating body complexity, statistical simulations towards dynamics of HIV, IoT-based wireless transmissions having malware spread were modeled and studied in detail [2] , [3] , [4] , [5] . The deliberation of human Corona due to spiked glycoprotein in the receptor-binding sites was studied [6] .…”

Section: Introductionmentioning

confidence: 99%

Nonlinear dynamics for the spread of pathogenesis of COVID-19 pandemic

Sharma

Bangia

Alshehri

et al. 2021

Journal of Infection and Public Health

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Coronaviruses did not invite attention at a global level and responsiveness until the series of 2003-SARS contagion followed by year- 2012 MERS plus, most recently, 2019- nCoV eruptions. SARS-CoV & MERS-CoV are painstaking, extremely pathogenic. Also, very evidently, both have been communicated from bats to palm-civets & dromedary camels and further transferred ultimately to humans. No country has been deprived of this viral genomic contamination wherever populaces reside and are interconnected. This study aimed to develop a mathematical model for calculating the transmissibility of this viral genome. The analysis aids the study of the outbreak of this Virus towards the other parts of the continent and the world. The parameters such as population mobility, natural history, epidemiological characteristics, and the transmission mechanism towards viral spread when considered into crowd dynamism result in improved estimation. This article studies the impact of time on the amount of susceptible, exposed, the infected person taking into account asymptomatic and symptomatic ones; recovered i.e., removed from this model and the virus particles existing in the open surfaces. The transition from stable phase to attractor phase happens after 13 days i.e.; it takes nearly a fortnight for the spread to randomize among people. Further, the pandemic transmission remains in the attractor phase for a very long time if no control measures are taken up. The attractor-source phase continues up to 385 days i.e., more than a year, and perhaps stabilizes on 386 th day as per the Lyapunov exponent's analysis. The time series helps to know the period of the Virus's survival in the open sources i.e. markets, open spaces and various other carriers of the Virus if not quarantined or sanitized. The Virus cease to exist in around 60 days if it does not find any carrier or infect more places, people etc. The changes in LCEs of all variables as time progresses for around 400 days have been forecasted. It can be observed that phase trajectories indicate how the two variables interact with each other and affect the overall system's dynamics. It has been observed that for exposed and asymptomatically infected ( y-z) , as exposed ones ( y ) change from 0 to 100 the value of asymptomatically infected ( z ) increased upto around 58, at exposed ones ( y ) =100, asymptomatically infected ( z ) has two values as 58 and 10 i.e. follows bifurcation and as exposed ones ( y ) changes values upto 180, the value of asymptomatically infected ( z ) decreases to 25 so for exposed ones ( y ) from 100 to 180, asymptomatically infected ( z ) varies from 58 to 25 to 10 follows bifurcation. Also, phase structure...

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“…In long term, it fills an individual with happiness, refreshes & improves health. Bhardwaj and Bangia [1] studies complex dynamics of meditating body using nonlinear analysis techniques. Bhardwaj et al [2][3][8][9] discussed different methodologies and forecasting techniques to predict air pollution, water-pollution and weather-forecasting with the impact of pollutants on health and the dynamics of environmental and health assessment.…”

Section: Introductionmentioning

confidence: 99%

Dynamical Indicator of Human Body’s Physical Endurance

Bhardwaj

Bangia

2021

Nep J. Math. Sci.

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Physical endurance is the time span between the beginning of physical activity by an individual and the termination because of exhaustion. Physical endurance involves a multifaceted behaviour which can be understood by complexities. Everyone performs physical activity in order to sustain-life. However, the number of activities done are largely subject to personal choice and varies from person to person as well as for a given person over time. Physical activity like meditation/exercises are positively related to physical fitness. One needs to understand relation between physical activity, exercise, physical fitness and health. These activities can be partitioned mutually exclusively into many different ways. This paper categorizes daily physical activity into three broad subdivisions based on amount of body movements taking place are: (i) light, (ii) moderate and (iii) high intensity. These three characterizations are considered to be mutually exclusive and sum up to total energy spent by an individual. The behavior of the three factors physical activity, heart and energy generated is analyzed with the help of Fast Lyapunov indicator (FLI), Dynamic Lyapunov indicator (DLI), Small alignment index (SALI). FLI’s increase for chaotic orbits for values of R=20, Q=70 for the case of high intensity exercises and to linearly regular orbits for values of R=5, Q=8 and R=10, Q=12 in the cases of light and moderate exercises respectively. SALI’s alters through non-zero value for R=20, Q=70 while it tends to zero for values of R=5, Q=8 and R=10, Q=12. DLI’s the largest Eigen values form a definite pattern/curve for n=2000 for values of R=5, Q=8 and n=100 for R=10, Q=12 respectively as the motion stays regular plus dispersed randomly as the motion is chaotic for n=60 and for R=20, Q=70.

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Complex Dynamics of Meditating Body

Cited by 9 publications

References 0 publications

Hybrid Fuzzified-PID Controller for non-linear Control Surfaces for DC Motor to Improve the Efficiency of Electric Battery Driven Vehicles

Hybrid Fuzzified-PID Controller for non-linear Control Surfaces for DC Motor to Improve the Efficiency of Electric Battery Driven Vehicles

Nonlinear dynamics for the spread of pathogenesis of COVID-19 pandemic

Dynamical Indicator of Human Body’s Physical Endurance

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