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This article concentrates in analyzing optimal controls for stochastic integrodifferential equation (SIDE) in Hilbert space. Necessary parameters are imposed to demonstrate the system that follows a unique variation of parameter formula using Leray Schauder Alternative. Subsequently, the existence of optimal control is investigated for the considered Lagrange control problem. The theoretical example with the mechanical example of ethanol fuelled engine are discussed to validate the results obtained.

Abstract This research presents a machine learning (ML)‐based model that determines the DC and RF characteristics of InGaAs sub‐channel double gate high electron mobility transistors (DG‐HEMTs) to optimize the device structure. We employ technology computer‐aided design (TCAD) simulations to analyze the DC and RF performance of InGaAs sub‐channel DG‐HEMTs, generating a range of datasets by varying the material composition, layer width, and thickness of different layers in the device structure. We then train and optimize support vector regression (SVR) models using 5‐fold cross‐validation, varying the kernel function and degree parameters, and achieve better performance with the radial basis function (RBF) kernel. The simulated results indicate that the ML model predicts physical parameters more effectively than experimental analysis, offering a compact modeling solution that requires fewer computing resources than traditional methods.

This paper focuses on a new class of non-instantaneous impulsive stochastic differential equations generated by mixed fractional Brownian motion with poisson jump in real separable Hilbert space. A set of sufficient conditions are generated based on the stochastic analysis technique, analytic semigroup theory of linear operators, fractional power of operators, and fixed point theory to obtain existence and uniqueness results of mild solutions for the considered system. Furthermore, the asymptotic behaviour of the system is investigated. Finally, an example is proposed to validate the obtained results.

In this manuscript, we investigate the approximate controllability for time-dependent impulsive neutral stochastic partial differential equations with fractional Brownian motion and memory in Hilbert space. By using semigroup theory, stochastic analysis techniques and fixed point approach, we derive a new set of sufficient conditions for the approximate controllability of nonlinear stochastic system under the assumption that the corresponding linear system is approximately controllable. Finally, an example is provided to illustrate our results.

Thin-walled box type conventional and composite structures are having wide applications for building the structural system which are used in advanced ships, aerospace, civil, construction equipment and etc. Often these structures are subjected to vibration and buckling due to the environmental effect such as mechanical, thermal, electrical, magnetic, and acoustic or a combination of these. Also dampingmaterial and structural stiffness plays an important role for the improvement of vibration, noise control, fatigue and bulking resistance of these structures. So it is important to know the dynamic and buckling characteristics of these structures. Pre-stress in a structure affects the stiffness, which modifies the dynamic and stability characteristics of the structure. So it is also important to know the influence of pre-stress on the vibration and buckling character. In this paper, buckling and dynamic characteristics of the thin-walled box type structures are analyzed using finite element software ANSYS.

Contribution: This article explores how the Industrial Internet of Things (IIoT) could be leveraged to enhance the teaching/learning experience of advanced control techniques [e.g., model-predictive control (MPC)] for complex systems (nonlinear and multivariable) for undergraduate students. Background: The IIoTs' features, such as ubiquitous sensing, open connectivity, and distributed control, are expected to transform the way control is implemented in the industries. The students need to be prepared for this development. Courses on advanced control techniques should be revamped considering this change. In particular, deploying advanced controllers in IIoT scenarios could make the students ready for Industrie 4.0 and enhance the teaching-learning experience. Intended Outcomes: To reduce the cost for setting up laboratories; to make students appreciate IIoT benefits in industries and study the enhancements in teaching/learning advanced control techniques, such as MPC. The focus is on implementing MPC for a complex process, i.e., nonlinear, multivariable, and having interactions and their deployment on IIoT hardware. Method: A ten-day course on IIoTs' benefits and implementing advanced control techniques for a complex process with lecturing and hands-on sessions for undergraduate students is used. The course focuses on understanding basic concepts to deploy advanced control techniques on IIoT hardware in industries. Findings: The learning experience is enthralling, and the students are appreciative of the IIoT benefits to the industries and in their learning experience, which is demonstrated by their in-depth understanding of concepts on system complexity and implementing MPC.