A NEW CHAOTIC ATTRACTOR GENERATED FROM A 3-D AUTONOMOUS SYSTEM WITH ONE EQUILIBRIUM AND ITS FRACTIONAL ORDER FORM

ABSTRACT In this paper, a novel three-dimensional autonomous chaotic system is proposed. The proposed system contains four variational parameters, a cubic nonlinearity term (i.e. product of all the three states) and exhibits a chaotic attractor in numerical simulations. The basic dynamic properties of the system are analyzed by means of equilibrium points, Eigen values and Lyapunov exponents. Finally, the commensurate and non-commensurate fractional order form of the system which exhibits chaotic attractor is also analyzed

Enhancing Infrastructure Safety: A UAV-Based Approach for Crack Detection

The imperative task of identifying and promptly detecting cracks in concrete bridges is crucial for preserving their structural health and ensuring the safety of users. Traditional bridge inspection methods heavily rely on human eyes and additional tools, demanding extensive training for inspectors and resulting in time-consuming processes. The increasing demand for Unmanned Aerial Vehicles (UAVs) has provided a transformative solution to access hard-to-reach areas efficiently. This research explores the integration of deep learning algorithms, including CNN, RCNN, Fast RCNN, Faster RCNN, and YOLO, to enhance the accuracy and efficiency of UAV-based crack detection systems. Experimental results affirm the effectiveness of these algorithms in addressing challenges such as lighting variations and small crack detection. The study aims to contribute to structural health monitoring, improving maintenance practices, and enhancing safety

Note on Fourier Transform of Hidden Variable Fractal Interpolation

This paper investigates the Fourier transform of a hidden variable fractal interpolation function with function scaling factors, which generalizes the Fourier transform of hidden variable fractal interpolation function with constant scaling factors. Furthermore, the Fourier transform of quadratic hidden variable fractal interpolation function with function scaling factors is also investigated. With an aim of maximizing the flexibility of hidden variable fractal interpolation function and quadratic hidden variable fractal interpolation function, a class of iterated function systems involving function scalings is chosen for the present study

Applied Fractional Calculus in Identification and Control

The book investigates the fractional calculus-based approaches and their benefits to adopting in complex real-time areas. Another objective is to provide initial solutions for new areas where fractional theory has yet to verify the expertise. The book focuses on the latest scientific interest and illustrates the basic idea of general fractional calculus with MATLAB codes. This book is ideal for researchers working on fractional calculus theory both in simulation and hardware. Researchers from academia and industry working or starting research in applied fractional calculus methods will find the book most useful. The scope of this book covers most of the theoretical and practical studies on linear and nonlinear systems using fractional-order integro-differential operators

Recent Advances and Applications of Fractional-Order Neural Networks

This paper focuses on the growth, development, and future of various forms of fractional-order neural networks. Multiple advances in structure, learning algorithms, and methods have been critically investigated and summarized. This also includes the recent trends in the dynamics of various fractional-order neural networks. The multiple forms of fractional-order neural networks considered in this study are Hopfield, cellular, memristive, complex, and quaternion-valued based networks. Further, the application of fractional-order neural networks in various computational fields such as system identification, control, optimization, and stability have been critically analyzed and discussed

Fuzzy Adaptive Setpoint Weighting Controller for WirelessHART Networked Control Systems

Gain range limitation of conventional proportional‐integral‐derivative (PID) controllers has made them unsuitable for application in a delayed environment. These controllers are also not suitable for use in a Wireless Highway Addressable Remote Transducer (WirelessHART) protocol networked control setup. This is due to stochastic network‐induced delay and uncertainties such as packet dropout. The use of setpoint weighting strategy has been proposed to improve the performance of the PID in such environments. However, the stochastic delay still makes it difficult to achieve optimal performance. This chapter proposes an adaptation to the setpoint weighting technique. The proposed approach will be used to adapt the setpoint weighting structure to variation in WirelessHART network‐induced delay through fuzzy inference. Result comparison of the proposed approach with both setpoint weighting and proportional‐integral (PI) control strategy shows improved setpoint tracking and load regulation. For the first‐, second‐ and third‐order systems considered, analysis of the results in the time domain shows that in terms of overshoot, undershoot, rise time, and settling times, the proposed approach outperforms both the setpoint weighting and the PI controller. The approach also shows faster recovery from disturbance effect

A Theoretical Approach to Optimize the Pipeline Data Communication in Oil and Gas Remote Locations Using Sky X Technology

Oil, gas, and water distribution networks in remote locations require optimized data transmission from their sources to prevent or detect leakage or improve production flow in their manufacturing units. Remote oil and gas installations frequently encounter substantial obstacles in terms of data connectivity and transfer. Slow data transmission rates, data loss, and decision-making delays can all be caused by a lack of dependable network infrastructure, restricted bandwidth, and severe climatic conditions. The purpose of this research work is to identify critical concerns concerning data communication and data transfer in oil and gas distant areas and to investigate feasible approaches to these challenges. The survey was carried out to gather feedback from oil and gas experts on issues concerning data transmission in remote locations. This study provides a theoretical approach to optimizing data transmission and communication in remote areas using Sky X technology. This study presents a new theoretical method that improves the performance of IP over satellite using the critical aspects of data transmission issues from experts. This technology's contribution can improve the reliability of all users on a satellite network by delivering all features with a successful data transfer rate discreetly. This attempt may also aid oil and gas companies in optimizing data transmission/communication in remote regions

Robustness and Stability Analysis of a Predictive PI Controller in WirelessHART Network Characterised by Stochastic Delay

As control over wireless network in the industry is receives increasing attention, its application comes with challenges such as stochastic network delay. The PIDs are ill equipped to handle such challenges while the model based controllers are complex. A settlement between the two is the PPI controller. However, there is no certainty on its ability to preserve closed loop stability under such challenges. While classical robustness measures do not require extensive uncertainty modelling, they do not guarantee stability under simultaneous process and network delay variations. On the other hand, the model uncertainty measures tend to be conservative. Thus, this work uses extended complementary sensitivity function method which handles simultaneously those challenges. Simulation results shows that the PPI controller can guarantee stability even under model and delay uncertainties

Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

Traditional statistical, physical, and correlation models for chaotic time series prediction have problems, such as low forecasting accuracy, computational time, and difficulty determining the neural network’s topologies. Over a decade, various researchers have been working with these issues; however, it remains a challenge. Therefore, this review paper presents a comprehensive review of significant research conducted on various approaches for chaotic time series forecasting, using machine learning techniques such as convolutional neural network (CNN), wavelet neural network (WNN), fuzzy neural network (FNN), and long short-term memory (LSTM) in the nonlinear systems aforementioned above. The paper also aims to provide issues of individual forecasting approaches for better understanding and up-to-date knowledge for chaotic time series forecasting. The comprehensive review table summarizes the works closely associated with the mentioned issues. It includes published year, research country, forecasting approach, application, forecasting parameters, performance measures, and collected data area in this sector. Future improvements and current studies in this field are broadly examined. In addition, possible future scopes and limitations are closely discussed