Search Results (100)

It is well known that the first stage of mine-to-mill optimization is rock fragmentation by blasting. The degree of rock fragmentation can be expressed in terms of average grain (X₅₀) size and size distribution. There are approaches in which exponential functions are used to estimate the size distribution of the pile that will be formed before blasting. The most common of these exponential functions used to estimate the average grain size is the Kuz–Ram and KCO functions. The exponential functions provide a curve from 0% to 100% using the mean grain size (X₅₀), characteristic size (X_C), and uniformity index (n) parameters. This distribution curve can make predictions in the range of fine grains and coarse grains outside the acceptable error limits in some cases. In this article, the usability of the hyperbolic tangent function, which is symmetrical at origin, in the estimation of the size distribution as an alternative to the exponential distribution functions used in almost all estimation models is investigated. As with exponential functions, the hyperbolic tangent function can express the aggregated size distribution as a percentage with reference to the variables X₅₀ and X_C. It has been shown that the hyperbolic tangent function provides 99% accuracy to the distribution of fine grains and coarse grains of the pile formed as a result of blasting data for the characteristic size (X_C) parameter and the uniformity index (n). Full article

The main aim of this study is to obtain soliton solutions of the generalized reaction Duffing model, which is a generalization for a collection of prominent models describing various key phenomena in science and engineering. The equation models the motion of a damped oscillator with a more complex potential than in basic harmonic motion. Two effective techniques, the mapping method and Bernoulli sub-ODE technique, are used for the first time to obtain the soliton solutions of the proposed model. Initially, the traveling wave transform, which comes from Lie symmetry infinitesimals, is applied, and a nonlinear ordinary differential equation form is derived. These approaches effectively retrieve a hyperbolic, Jacobi function as well as trigonometric solutions while the appropriate conditions are applied to the parameters. Numerous innovative solutions, including the kink wave, anti-kink wave, bell shape, anti-bell shape, W-shape, bright, dark and singular shape soliton solutions, were produced via the mapping and Bernoulli sub-ODE approaches. The research includes comprehensive 2D and 3D graphical representations of the solutions, facilitating a better understanding of their physical attributes and proving the effectiveness of the proposed methods in solving complex nonlinear equations. It is important to note that the proposed methods are competent, credible and interesting analytical tools for solving nonlinear partial differential equations. Full article

The time-fractional coupled Drinfel’d–Sokolov–Wilson (DSW) equation is pivotal in soliton theory, especially for water wave mechanics. Its precise description of soliton phenomena in dispersive water waves makes it widely applicable in fluid dynamics and related fields like tsunami prediction, mathematical physics, and plasma physics. In this study, we present novel soliton solutions for the DSW equation, which significantly enhance the accuracy of describing soliton phenomena. To achieve these results, we employed two distinct methods to derive the solutions: the Sardar subequation method, which works with one variable, and the $\left(\frac{{\mathsf{\Omega }}^{\prime }}{\mathsf{\Omega }}, \frac{1}{\mathsf{\Omega }}\right)$ method which utilizes two variables. These approaches supply significant improvements in efficiency, accuracy, and the ability to explore a broader spectrum of soliton solutions compared to traditional computational methods. By using these techniques, we construct a wide range of wave structures, including rational, trigonometric, and hyperbolic functions. Rigorous validation with Mathematica software 13.1 ensures precision, while dynamic visual representations illustrate soliton solutions with diverse patterns such as dark solitons, multiple dark solitons, singular solitons, multiple singular solitons, kink solitons, bright solitons, and bell-shaped patterns. These findings highlight the effectiveness of these methods in discovering new soliton solutions and supplying deeper insights into the DSW model’s behavior. The novel soliton solutions obtained in this study significantly enhance our understanding of the DSW equation’s underlying dynamics and offer potential applications across various scientific fields. Full article

Complex aviation systems’ integrity deteriorates over time due to operational factors; hence, the ability to forecast component remaining useful life (RUL) is vital to their optimal operation. Data-driven prognostic models are essential for system RUL prediction. These models benefit run-to-failure datasets the most. Thus, significant factors that could affect systematic integrity must be examined to quantify the operational component of RUL. To expand predictive approaches, the authors of this research developed a novel method for calculating the RUL of a group of aircraft engines using the N-CMAPSS dataset, which provides simulated degradation trajectories under real flight conditions. They offered bootstrap trees and hyperbolic tangent NtanH(3)Boost(20) neural networks as prognostic alternatives. The hyperbolic tangent boosted neural network uses damage propagation modelling based on earlier research and adds two accuracy levels. The suggested neural network architecture activates with the hyperbolic tangent function. This extension links the deterioration process to its operating history, improving degradation modelling. During validation, models accurately predicted observed flight cycles with 95–97% accuracy. We can use this work to combine prognostic approaches to extend the lifespan of critical aircraft systems and assist maintenance approaches in reducing operational and environmental hazards, all while maintaining normal operation. The proposed methodology yields promising results, making it suitable for adoption due to its relevance to prognostic difficulties. Full article

This paper studies distributed optimal control for non-cooperative systems involving time-fractional hyperbolic operators. Through the application of the Lax–Milgram theorem, we confirm the existence and uniqueness of weak solutions. Central to our approach is the utilization of the linear quadratic cost functional, which is meticulously crafted to encapsulate the interplay between the system’s state and control variables. This functional serves as a pivotal tool in imposing constraints on the dynamic system under consideration, facilitating a nuanced understanding of its controllability. Using the Euler–Lagrange first-order optimality conditions with an adjoint problem defined by means of the right-time fractional derivative in the Caputo sense, we obtain an optimality system for the optimal control. Finally, some examples are analyzed. Full article

As technology evolves, more complex non-affine systems are created. These complex systems are hard to model, whereas most controllers require information on systems to be designed. This information is hard to obtain for systems with varying control directions. Therefore, this study introduces a novel data-driven estimator and controller tailored for single-input single-output non-affine discrete-time systems. This approach focuses on cases when the control direction varies over time and the mathematical model of the system is completely unknown. The estimator and controller are constructed using a Multiple-input Fuzzy Rules Emulated Network framework. The weight vectors are updated through the gradient descent optimization method, which employs a unique cost function that multiplies the error by a hyperbolic tangent. The stability analyses demonstrate that both the estimator and controller converge to uniformly ultimately bounded (UUB) functions of Lyapunov. To validate the results, we show experimental tests of force control that were executed on the z-axis of a drive-controlled 3D scanning robot. This system has a varying control direction, and we also provide comparison results with a state-of-the-art controller. The results show a mean absolute percentage tracking error smaller than one percent on the steady state and the expected variation in the system’s control direction. Full article

While traditional support vector regression (SVR) models rely on loss functions tailored to specific noise distributions, this research explores an alternative approach: $\epsilon$-ln SVR, which uses a loss function based on the natural logarithm of the hyperbolic cosine function (lncosh). This function exhibits optimality for a broader family of noise distributions known as power-raised hyperbolic secants (PHSs). We derive the dual formulation of the $\epsilon$-ln SVR model, which reveals a nonsmooth, nonlinear convex optimization problem. To efficiently overcome these complexities, we propose a novel sequential minimal optimization (SMO)-like algorithm with an innovative working set selection (WSS) procedure. This procedure exploits second-order (SO)-like information by minimizing an upper bound on the second-order Taylor polynomial approximation of consecutive loss function values. Experimental results on benchmark datasets demonstrate the effectiveness of both the $\epsilon$-ln SVR model with its lncosh loss and the proposed SMO-like algorithm with its computationally efficient WSS procedure. This study provides a promising tool for scenarios with different noise distributions, extending beyond the commonly assumed Gaussian to the broader PHS family. Full article

This paper delves into a comprehensive study of a wide-speed-range sensorless control approach for surface-mounted permanent magnet synchronous motors (SPMSMs). In the low-speed range, a novel high-frequency pulse voltage injection (HFPVI) method is introduced for rotor position estimation, which does not depend on motor saliency and is well-suited for SPMSMs. This method incorporates a second-order generalized integrator (SOGI) and a new modulation signal to enhance the accuracy of rotor position estimation. For medium-to-high speeds, an improved super-twisting sliding mode observer (STSMO) utilizing a continuous hyperbolic tangent function is proposed to mitigate chattering. Additionally, a new phase-locked loop (NPLL) is introduced to accurately obtain the rotor position. Furthermore, this paper designs an exponential weighted switching function to facilitate a smooth transition of the motor from the low-speed domain to the medium- and high-speed domains. The effectiveness and superiority of the proposed methods are validated through simulations and experiments conducted on an RTU-BOX platform. The rotor position estimation errors of the proposed new HFPVI method and the improved STSMO method under various operating conditions are both approximately 0.05 rad (2.8 elc·deg), and the SPMSM can switch smoothly from the low-speed range to the medium- and high-speed ranges. Full article

This paper demonstrates the utilization of Pontryagin Neural Networks (PoNNs) to acquire control strategies for achieving fuel-optimal trajectories. PoNNs, a subtype of Physics-Informed Neural Networks (PINNs), are tailored for solving optimal control problems through indirect methods. Specifically, PoNNs learn to solve the Two-Point Boundary Value Problem derived from the application of the Pontryagin Minimum Principle to the problem’s Hamiltonian. Within PoNNs, the Extreme Theory of Functional Connections (X-TFC) is leveraged to approximate states and costates using constrained expressions (CEs). These CEs comprise a free function, modeled by a shallow neural network trained via Extreme Learning Machine, and a functional component that consistently satisfies boundary conditions analytically. Addressing discontinuous control, a smoothing technique is employed, substituting the sign function with a hyperbolic tangent function and implementing a continuation procedure on the smoothing parameter. The proposed methodology is applied to scenarios involving fuel-optimal Earth−Mars interplanetary transfers and Mars landing trajectories. Remarkably, PoNNs exhibit convergence to solutions even with randomly initialized parameters, determining the number and timing of control switches without prior information. Additionally, an analytical approximation of the solution allows for optimal control computation at unencountered points during training. Comparative analysis reveals the efficacy of the proposed approach, which rivals state-of-the-art methods such as the shooting technique and the adaptive Gaussian quadrature collocation method. Full article

Early detection of ransomware attacks is critical for minimizing the potential damage caused by these malicious attacks. Feature selection plays a significant role in the development of an efficient and accurate ransomware early detection model. In this paper, we propose an enhanced Mutual Information Feature Selection (eMIFS) technique that incorporates a normalized hyperbolic function for ransomware early detection models. The normalized hyperbolic function is utilized to address the challenge of perceiving common characteristics among features, particularly when there are insufficient attack patterns contained in the dataset. The Term Frequency–Inverse Document Frequency (TF–IDF) was used to represent the features in numerical form, making it ready for the feature selection and modeling. By integrating the normalized hyperbolic function, we improve the estimation of redundancy coefficients and effectively adapt the MIFS technique for early ransomware detection, i.e., before encryption takes place. Our proposed method, eMIFS, involves evaluating candidate features individually using the hyperbolic tangent function (tanh), which provides a suitable representation of the features’ relevance and redundancy. Our approach enhances the performance of existing MIFS techniques by considering the individual characteristics of features rather than relying solely on their collective properties. The experimental evaluation of the eMIFS method demonstrates its efficacy in detecting ransomware attacks at an early stage, providing a more robust and accurate ransomware detection model compared to traditional MIFS techniques. Moreover, our results indicate that the integration of the normalized hyperbolic function significantly improves the feature selection process and ultimately enhances ransomware early detection performance. Full article

In addressing the control of asynchronous motors in the remote conductivity of fertigation machines, this study proposes a joint control strategy based on the Fast Terminal Sliding Mode Control-Disturbance Observer (FTSMC-DO) system for asynchronous motors. The goal is to enhance the dynamic performance and disturbance resistance of asynchronous motors, particularly under low-speed operating conditions. The approach involves refining the two-degree-of-freedom internal model controller using fractional-order functions to explicitly separate the controller’s robustness and tracking capabilities. To mitigate the motor’s sensitivity to external disturbances during variable speed operations, a load disturbance observer is introduced, employing hyperbolic tangent and Fal functions for real-time monitoring and compensation, seamlessly integrated into the sliding mode controller. To address issues related to low-speed chattering typically associated with sliding mode controllers, this study introduces a revised non-singular fast terminal sliding mode surface. Additionally, guided by fuzzy control principles, the study enables real-time selection of sliding mode approaching law parameters. Experimental results from the asynchronous motor control platform demonstrate that FTSMC-DO control significantly reduces adjustment time and speed fluctuations during operation, minimizing the impact of load disturbances on the system. The system exhibits robust disturbance rejection, improved robustness, and enhanced control capability. Furthermore, field tests validate the effectiveness of the FTSMC-DO system in regulating remote electrical conductivity (EC) levels. The control time is observed to be less than 120 s, overshoot less than 16.1%, and EC regulation within 0.2 mS·cm⁻¹ over a pipeline distance of 120 m. The FTSMC-DO control consistently achieves the desired EC levels with minimal fluctuation and overshoot, outperforming traditional PID and SMC methods. This high level of precision is crucial for ensuring optimal nutrient delivery and efficient water usage in agricultural irrigation systems, highlighting the system’s potential as a valuable tool in modern, sustainable farming practices. Full article

This paper proposes a sliding mode synchronous control approach to enhance the position synchronization performance and anti-interference capability of a double lifting point hydraulic hoist. Building upon the cross-coupling synchronous control method, a coupling sliding mode surface is formulated, incorporating the single-cylinder following error and double-cylinder synchronization error. Additionally, a sliding mode synchronous controller is devised to ensure the convergence of both the single-cylinder following and synchronization error. The hyperbolic tangent function is introduced to reduce the single-cylinder following error and the buffeting of the double-cylinder synchronization error curve under sliding mode synchronous control. The simulation results show that the synchronization accuracy of the sliding mode cross-coupling synchronization control in the initial stage of the system is 53.1% higher than that of the Proportional-Derivative (PD) cross-coupling synchronization, and the synchronization accuracy in the steady state of the system is improved by 90%. The designed synchronous controller has better performance under external disturbances. Full article

The increasing prevalence of unknown-type attacks on the Internet highlights the importance of developing efficient intrusion detection systems. While machine learning-based techniques can detect unknown types of attacks, the need for innovative approaches becomes evident, as traditional methods may not be sufficient. In this research, we propose a deep learning-based solution called the log-cosh variational autoencoder (LVAE) to address this challenge. The LVAE inherits the strong modeling abilities of the variational autoencoder (VAE), enabling it to understand complex data distributions and generate reconstructed data. To better simulate discrete features of real attacks and generate unknown types of attacks, we introduce an effective reconstruction loss term utilizing the logarithmic hyperbolic cosine (log-cosh) function in the LVAE. Compared to conventional VAEs, the LVAE shows promising potential in generating data that closely resemble unknown attacks, which is a critical capability for improving the detection rate of unknown attacks. In order to classify the generated unknown data, we employed eight feature extraction and classification techniques. Numerous experiments were conducted using the latest CICIDS2017 dataset, training with varying amounts of real and unknown-type attacks. Our optimal experimental results surpassed several state-of-the-art techniques, achieving accuracy and average F1 scores of 99.89% and 99.83%, respectively. The suggested LVAE strategy also demonstrated outstanding performance in generating unknown attack data. Overall, our work establishes a solid foundation for accurately and efficiently identifying unknown types of attacks, contributing to the advancement of intrusion detection techniques. Full article

This work uses a mixture theory approach to describe kinematically constrained flows through porous media using an adequate constitutive relation for pressure that preserves the problem hyperbolicity even when the flow becomes saturated. This feature allows using the same mathematical tool for handling unsaturated and saturated flows. The mechanical model can represent the saturated–unsaturated transition and vice-versa. The constitutive relation for pressure is a continuous and differentiable function of saturation: an increasing function with a strictly convex, increasing, and positive first derivative. This significant characteristic permits the fluid to establish a tiny controlled supersaturation of the porous matrix. The associated Riemann problem’s complete solution is addressed in detail, with explicit expressions for the Riemann invariants. Glimm’s semi-analytical scheme advances from a given instant to a subsequent one, employing the associated Riemann problem solution for each two consecutive time steps. The simulations employ a variation in Glimm’s scheme, which uses the mean of four independent sequences for each considered time, ensuring computational solutions with reliable positions of rarefaction and shock waves. The results permit verifying this significant characteristic. Full article

In this research work, we investigate the complex structure of soliton in the Fractional Kudryashov–Sinelshchikov Equation (FKSE) using conformable fractional derivatives. Our study involves the development of soliton solutions using the modified Extended Direct Algebraic Method (mEDAM). This approach involves a key variable transformation, which successfully transforms the model into a Nonlinear Ordinary Differential Equation (NODE). Following that, by using a series form solution, the NODE is turned into a system of algebraic equations, allowing us to construct soliton solutions methodically. The FKSE is the governing equation, allowing for heat transmission and viscosity effects while capturing the behaviour of pressure waves in liquid–gas bubble mixtures. The solutions we discover include generalised trigonometric, hyperbolic, and rational functions with kinks, singular kinks, multi-kinks, lumps, shocks, and periodic waves. We depict two-dimensional, three-dimensional, and contour graphs to aid comprehension. These newly created soliton solutions have far-reaching ramifications not just in mathematical physics, but also in a wide range of subjects such as optical fibre research, plasma physics, and a variety of applied sciences. Full article