3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-Controlled GWO-AOA

Unmanned Aerial Vehicles(UAVs)or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance,due to their ability to perform repetitive and tedious tasks in hazardous environments.Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional(3D)flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature.However,not a single optimization algorithms can solve all kind of optimization problem effectively.Therefore,there is dire need to integrate metaheuristic for general acceptability.To address this issue,in this paper,a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm(QGA)has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then,utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment.The performance of the developed QGA has been compared against the various metaheuristics.The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.

Parallel Fuzzy P+Fuzzy I+Fuzzy D Controller:Design and Performance Evaluation

In this paper, a parallel fuzzy proportional plus fuzzy integral plus fuzzy derivative （FP＋FI＋FD） controller is proposed. It is derived from the conventional parallel proportional-integral-derivative （PID） controller. It preserves the linear structure of a conventional parallel PID controller, with analytical formulas. The final shape of the controller is a discrete-time fuzzy version of a conventional parallel PID controller. Computer simulations are performed to evaluate the performance of the FP＋FI＋FD controller for setpoint tracking and load-disturbance rejection for some complex processes, such as first- and second-order processes with delay, inverse response process with and without delay and higher order processes. Also, the performance of the proposed fuzzy controller is evaluated experimentally on highly nonlinear liquid-flow process with a hysteresis characteristic due to a pneumatic control valve. The simulation and real time control is done using National InstrumentTM hardware and software （LabVIEWTM）. The response of the FP＋FI＋FD controller is compared with the conventional parallel PID controller, tuned with the Ziegler-Nichols （Z-H） and /~strSm- H^gglund （A-H） tuning technique. It is observed that the FP＋FI＋FD controller performed much better than the conventional PI/PID controller. Simulation and experimental results demonstrate the effectiveness of the proposed parallel FP＋FI＋FD controller.

Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

The COVID-19 outbreak initiated from the Chinese city of Wuhanand eventually affected almost every nation around the globe. From China,the disease started spreading to the rest of the world. After China, Italybecame the next epicentre of the virus and witnessed a very high death toll.Soon nations like the USA became severely hit by SARS-CoV-2 virus. TheWorld Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the worldhas instituted various policies like physical distancing, isolation of infectedpopulation and researching on the potential vaccine of SARS-CoV-2. Toidentify the impact of various policies implemented by the affected countrieson the pandemic spread, a myriad of AI-based models have been presented toanalyse and predict the epidemiological trends of COVID-19. In this work, theauthors present a detailed study of different articial intelligence frameworksapplied for predictive analysis of COVID-19 patient record. The forecastingmodels acquire information from records to detect the pandemic spreadingand thus enabling an opportunity to take immediate actions to reduce thespread of the virus. This paper addresses the research issues and correspondingsolutions associated with the prediction and detection of infectious diseaseslike COVID-19. It further focuses on the study of vaccinations to cope withthe pandemic. Finally, the research challenges in terms of data availability,reliability, the accuracy of the existing prediction models and other open issuesare discussed to outline the future course of this study.

Influence of stimuli color on steady-state visual evoked potentials based BCI wheelchair control

In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attention. This study tries to develop a SSVEP based BCI system that can control a wheelchair prototype in five different positions including stop position. In this study four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using Lab-VIEW. Four stimuli colors, green, red, blue and violet were used to investigate the color influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were segmented into 1 second window and features were extracted by using Fast Fourier Transform (FFT). One-Against-All (OAA), a popular strategy for multiclass SVM, is used to classify SSVEP signals. During stimuli color comparison SSVEP with violet color showed higher accuracy than that with green, red and blue stimuli.

A robust test of uncertain linear systems

In this paper, it is shown that for low-order uncertain systems, there is no need to calculate all the minimum and maximum values of the coefficients for a perturbed system which is expressed in terms of polynomials and hence no need to formulate and test all the four Kharitonov＇s polynomials. Furthermore, for higher-order systems such as n ≥ 5, the usual four Kharitonov＇s polynomials need not be tested initially for sufficient condition of perturbed systems; rather, the necessary condition can be checked before going for sufficient condition. In order to show the effectiveness of the proposed method, numerical examples are shown and computational efficiency is highlighted.

Detection of Fuel Adulteration Using Wave Optical with Machine Learning Algorithms

Fuel is a very important factor and has considerable influence on the air quality in the environment,which is the heart of the world.The increase of vehi-cles in lived-in areas results in greater emission of carbon particles in the envir-onment.Adulterated fuel causes more contaminated particles to mix with breathing air and becomes the main source of dangerous pollution.Adulteration is the mixing of foreign substances in fuel,which damages vehicles and causes more health problems in living beings such as humans,birds,aquatic life,and even water resources by emitting high levels of hydrocarbons,nitrogen oxides,and carbon monoxide.Most frequent blending liquids are lubricants and kerosene in the petrol,and its adulteration is a considerable problem that adds to environ-mental pollution.This study focuses on detecting the adulteration in petrol using sensors and machine learning algorithms.A modified evanescent wave opticalfiber sensor with discrete wavelet transform is proposed for classification of adult-erated data from the samples.Furthermore,support vector machine classifier is used for accurate categorization.The sensor isfirst tested with fuel and numerical data is classified based on machine learning algorithms.Finally,the result is eval-uated with less error and high accuracy of 99.9%,which is higher than all existing techniques.

Modeling Stress Intensity Factor of Rail Steel under Situation of Growing Fatigue Crack—A Novel Technique

Simulation of stress intensity factor as function of rolling contact fatigue cracks of railway tracks and the vehicle load is made with the help of COMSOL Multiphysics software. It is found that the critical stress intensity factor i.e. 41.6 MPa. m1/2 is reached at a stress level of 32 MPa and at the crack size 11.5 × 10-2 m.Noting the power law variation of acoustic emission count with increase in crack size (analogous to Paris Law), the simulation was further carried out to model the dependence of measured AE count with the stress intensity factor ahead of a growing RCF crack tip. It is demonstrated that AE measurement can be effective to trigger a control loop for avoidance of fatigue failure of railway track. In view of potential difference in the intensity of back scattered light from surface irregularities, a model is developed to find out the threshold intensity of scattered light that insures safety in the railway system against fatigue failure.

Design of internal model control based fractional order PID controller

This article presents a design of the internal model control （IMC） based single degree of freedom （SDF） fractional order （FO） PID controller with a desired bandwidth specification for a class of fractional order system （FOS）. The drawbacks of the SDF FO-IMC are eliminated with the help of the two-degree of freedom （TDF） FO PID controller. The robust stability and robust performance of the designed controller are analyzed using an example.

Fuzzy-based metaheuristic algorithm for optimization of fuzzy controller:fault-tolerant control application

Purpose-Fuzzy-based metaheuristic algorithm is used to optimize the fuzzy controllers for the nonlinear level control system subject to uncertainty specially in the main actuator that has lost effectiveness(LOE).To optimize the fuzzy controller,type-1 harmonic search(HS)and interval type-2(HS)will be used.Design/methodology/approach-The type-1 and type-2 fuzzy-based HS algorithms are designed for optimization of fuzzy controllers for Fault-Tolerant Control(FTC)applications,and this research proposes a fuzzy-based HS metaheuristic method.The performance of a fuzzy logic-based HS algorithm applied to a nonlinear two-tank level control process with a main actuator that has lost effectiveness(LOE)and also the same controller will be tested on DC motor angular position control with and without noise.Findings-The key contribution of this work is the discovery of the best approach for generating an optimal vector of values for the fuzzy controller’s membership function optimization.This is done in order to improve the controller’s performance,bringing the process value of the two-tank level control process closer to the target process value(set point).It is worth noting that the type-1 fuzzy controller that has been optimized is an interval type-2 fuzzy system,which can handle more uncertainty than a type-1 fuzzy system.Originality/value-The type-1 and type-2 fuzzy-based HS algorithms are designed for optimization of fuzzy controllers for FTC applications,and this research proposes a fuzzy-based HS metaheuristic method.The performance of a fuzzy logic-based HS algorithm applied to a nonlinear two-tank level control process with a main actuator that has LOE will be tested on DC motor angular position control with noise.Two nonlinear uncertain processes are used to demonstrate the effectiveness of the proposed control scheme.

Energy Aware Metaheuristic Optimization with Location Aided Routing Protocol for MANET

A mobile ad hoc network(MANET)involves a group of wireless mobile nodes which create an impermanent network with no central authority and infrastructure.The nodes in the MANET are highly mobile and it results in adequate network topology,link loss,and increase the re-initialization of the route discovery process.Route planning in MANET is a multi-hop communication process due to the restricted transmission range of the nodes.Location aided routing(LAR)is one of the effective routing protocols in MANET which suffers from the issue of high energy consumption.Though few research works have focused on resolving energy consumption problem in LAR,energy efficiency still remains a major design issue.In this aspect,this study introduces an energy aware metaheuristic optimization with LAR(EAMO-LAR)protocol for MANETs.The EAMO-LAR protocol makes use of manta ray foraging optimization algorithm(MRFO)to help the searching process for the individual solution to be passed to the LAR protocol.The fitness value of the created solutions is determined next to pass the solutions to the objective function.The MRFO algorithm is incorporated into the LAR protocol in the EAMO-LAR protocol to reduce the desired energy utilization.To ensure the improved routing efficiency of the proposed EAMO-LAR protocol,a series of simulations take place.The resultant experimental values pointed out the supreme outcome of the EAMO-LAR protocol over the recently compared methods.The resultant values demonstrated that the EAMO-LAR protocol has accomplished effectual results over the other existing techniques.

Brain initiated interaction

Brain-Computer Interfaces (BCI) are developed to help locked-in patients, who lose control of their bodies and are unable to perform simple tasks such as speech, locomotion, and can’t even effectively interact, with their environment. BCI shows promise in allowing these individuals to interact with a computer using EEG. A Brain Computer Interface is a communication system in which messages or commands that an indi-vidual sends to the external world do not pass through the brain’s normal output pathways of peripheral nerves and muscles. A system is created to allow individuals with motor disabili-ties to control the motion of the bed on which they are bedridden via BCI for drug delivery and other activities, with the help of eye motion and changes in the absolute power in alpha rhythms of an EEG signal of the patient.

Type-2 fuzzy logic applications designed for active parameter adaptation in metaheuristic algorithm for fuzzy fault-tolerant controller

Purpose-In recent times,fuzzy logic is gaining more and more attention,and this is because of the capability of understanding the functioning of the system as per human knowledge-based system.The main contribution of the work is dynamically adapting the important parameters throughout the execution of the flower pollination algorithm(FPA)using concepts of fuzzy logic.By adapting the main parameters of the metaheuristics,the performance and accuracy of the metaheuristic have been improving in a varied range of applications.Design/methodology/approach-The fuzzy logic-based parameter adaptation in the FPA is proposed.In addition,type2 fuzzy logic is used to design fuzzy inference system for dynamic parameter adaptation in metaheuristics,which can help in eliminating uncertainty and hence offers an attractive improvement in dynamic parameter adaption in metaheuristic method,and,in reality,the effectiveness of the interval type2 fuzzy inference system(IT2 FIS)has shown to provide improved results as matched to type-1 fuzzy inference system(T1 FIS)in some latest work.Findings-One case study is considered for testing the proposed approach in a fault tolerant control problem without faults and with partial loss of effectiveness of main actuator fault with abrupt and incipient nature.For comparison between the type-1 fuzzy FPA and interval type-2 fuzzy FPA is presented using statitical analysis which validates the advantages of the interval type2 fuzzy FPA.The statistical Z-test is presented for comparison of efficiency between two fuzzy variants of the FPA optimization method.Originality/value-The main contribution of the work is a dynamical adaptation of the important parameters throughout the execution of the flower pollination optimization algorithm using concepts of type2 fuzzy logic.By adapting the main parameters of the metaheuristics,the performance and accuracy of the metaheuristic have been improving in a varied range of applications.

A simple approach for stability margin of discrete systems

In this paper, a technique is presented to determine the stability margin of the discrete systems using recursive algorithm for power of companion matrix and Gerschgorin Theorem and hence sufficient condition of stability is obtained. The method is illustrated with an example and it is compared with other methods proposed in the literature. The results have applications in the filter design.

Flow-directed assembly of non-spherical titania nanoparticles into superhydrophilic thin films

Superhydrophilic thin films of 21 nm sized non-spherical titania nanoparticles are fabricated from a colloidal suspension by fixed blade flow coating without UV illumination. At a blade angle of a= 36° and a gap of d= 300 μm, hierarchically structured films with increasing surface roughness along with microscopic voids are formed depending on the substrate velocity and the titania volume fraction. Increasing the roughness is shown to be concomitant to an increase in the hydrophilicity, eventually leading to superhydrophilicity or water contact angle less than 5°.

基于队列智能方法的分数阶PI~λD~μ控制器设计（英文）

近年来,队列智能方法逐步演变为一种基于人工智能的优化方法。本文首次采用队列智能方法对分数阶PID控制器的参数进行优化。验证了队列智能方法在分数阶PID控制器设计中的性能,并与其他方法如粒子群优化方法、遗传算法和改进的电磁算法等的性能进行比较。结果表明,与其它3种算法相比,队列智能方法在代价函数、计算时间和函数评估方面表现出更好性能。此外,队列智能方法对应的标准差表明,提出的方法在解决控制问题时具有鲁棒性。