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The present automotive vehicles have almost 75 percentage of electronics part for many applications such as air bag system, door opening system, door lock systems. The proposed system focuses on measuring various physical system’s sensor readings and predicting other subsystem function by calibration. If any hardware system gets futile in the vehicle or if any dangerous gas is released or if engine temperature becomes high by load current, the fault can be identified. The sensor readings are uninterruptedly transmitted to the dash board part via the engine part core. In dash board, comparison of the threshold is done to identify sensor failures. The trouble shooting information to the user is displayed in the dash board. This proposed work will sustain a vehicle system to be in good working condition to overcome accidents.

A transparent digital twin (DT) is designed for output control using the belief rule base (BRB), namely, DT-BRB. The goal of the transparent DT-BRB is not only to model the complex relationships between the system inputs and output but also to conduct output control by identifying and optimizing the key parameters in the model inputs. The proposed DT-BRB approach is composed of three major steps. First, BRB is adopted to model the relationships between the inputs and output of the physical system. Second, an analytical procedure is proposed to identify only the key parameters in the system inputs with the highest contribution to the output. Being consistent with the inferencing, integration, and unification procedures of BRB, there are also three parts in the contribution calculation in this step. Finally, the data-driven optimization is performed to control the system output. A practical case study on the Wuhan Metro System is conducted for reducing the building tilt rate (BTR) in tunnel construction. By comparing the results following different standards, the 80% contribution standard is proved to have the highest marginal contribution that identifies only 43.5% parameters as the key parameters but can reduce the BTR by 73.73%. Moreover, it is also observed that the proposed DT-BRB approach is so effective that iterative optimizations are not necessarily needed.

We present a novel Digital Twin Framework for a portable table-top sized temperature-controlled system constructed entirely using free and open-source software programs. By utilizing tools such as Robotic Process Automation (RPA) and a PLC program, the entire process was automated to achieve a true Digital Twin. Further, a mathematical model was constructed for the temperature-controlled system. The data from the physical model and the mathematical model was then compared and a what-if scenario was analyzed to predict and prevent a potential failure in the physical system. Additionally, a simulation model was created to visualize the results.

The output constraints are widespread in physical systems. Violation of output constraints may result in system damage and performance degradation. This paper investigates the design issue of adaptive fuzzy second-order sliding mode (SOSM) controller, which aims to handle a class of nonlinear systems with output constraints. The unknown bounds of uncertainties are approached dynamically by fuzzy logic systems. Through designing a new barrier Lyapunov function, the output constraint problem has been well solved. Then, by integrating adding a power integrator (API) technology and adaptive fuzzy control, a novel adaptive fuzzy SOSM controller is proposed. It is proved that the proposed method makes the output variable not violate the specified constraint region. At the same time, it can be shown based upon the Lyapunov approach that the finite-time stability of the resulting closed-loop system under output constraint is ensured. Finally, a numerical example and a practical pendulum system are presented to demonstrate the validity of the proposed SOSM control strategy.

Reservoir computing has emerged as a powerful machine learning paradigm for harvesting nontrivial information processing out of disordered physical systems driven by sequential inputs. To this end, the system observables must become nonlinear functions of the input history. We show that encoding the input to quantum or classical fluctuations of a network of interacting harmonic oscillators can lead to a high performance comparable to that of a standard echo state network in several nonlinear benchmark tasks. This equivalence in performance holds even with a linear Hamiltonian and a readout linear in the system observables. Furthermore, we find that the performance of the network of harmonic oscillators in nonlinear tasks is robust to errors both in input and reservoir observables caused by external noise. For any reservoir computing system with a linear readout, the magnitude of trained weights can either amplify or suppress noise added to reservoir observables. We use this general result to explain why the oscillators are robust to noise and why having precise control over reservoir memory is important for noise robustness in general. Our results pave the way toward reservoir computing harnessing fluctuations in disordered linear systems.

Nuclear fusion is a promising alternative to address the problem of sustainable energy production. The tokamak is an approach to fusion based on magnetic plasma confinement, constituting a complex physical system with many control challenges. We study the characteristics and optimization of reservoir computing (RC) for real-time and adaptive prediction of plasma profiles in the DIII-D tokamak. Our experiments demonstrate that RC achieves comparable results to state-of-the-art (deep) convolutional neural networks (CNNs) and long short-term memory (LSTM) models, with a significantly easier and faster training procedure. This efficient approach allows for fast and frequent adaptation of the model to new situations, such as changing plasma conditions or different fusion devices.

Experiments aimed at searching for variations in the fine-structure constant α are based on spectroscopy of transitions in microscopic bound systems, such as atoms and ions, or resonances in optical cavities. The sensitivities of these systems to variations in α are typically on the order of unity and are fixed for a given system. For heavy atoms, highly charged ions and nuclear transitions, the sensitivity can be increased by benefiting from the relativistic effects and favorable arrangement of quantum states. This article proposes a new method for controlling the sensitivity factor of macroscopic physical systems. Specific concepts of optical cavities with tunable sensitivity to α are described. These systems show qualitatively different properties from those of previous studies of the sensitivity of macroscopic systems to variations in α, in which the sensitivity was found to be fixed and fundamentally limited to an order of unity. Although possible experimental constraints attainable with the specific optical cavity arrangements proposed in this article do not yet exceed the present best constraints on α variations, this work paves the way for developing new approaches to searching for variations in the fundamental constants of physics.

Detecting anomalies and attacks in smart cyber-physical systems are of paramount importance owing to their growing prominence in controlling critical systems. However, this is a challenging task due to the heterogeneity and variety of components of a CPS, and the complex relationships between sensed values and potential attacks or anomalies. Such complex relationships are results of physical constraints and domain norms which exist in many CPS domains. In this paper, we propose ABATe, an Automatic Behavioral Abstraction Technique based on Neural Networks for detecting anomalies in smart cyber-physical systems. Unlike traditional techniques which abstract the statistical properties of different sensor values, ABATe learns complex relationships between event vectors from normal operational data available in abundance with smart CPS and uses this abstracted model to detect anomalies. ABATe detected more than 88% of attacks in the publicly available SWaT dataset featuring data from a scaled down Sewage Water Treatment plant with a very low false positive rate of 1%. We also evaluated our technique's ability to capture domain semantics and multi-domain adaptability using a real-world automotive dataset, as well as a synthetic dataset.

Nonlinear evolution equations (NLEEs) are primarily relevant to nonlinear complex physical systems in a wide range of fields, including ocean physics, plasma physics, chemical physics, optical fibers, fluid dynamics, biology physics, solid-state physics, and marine engineering. This paper investigates the Lie symmetry analysis of a generalized (3+1)-dimensional breaking soliton equation depending on five nonzero real parameters. We derive the Lie infinitesimal generators, one-dimensional optimal system, and geometric vector fields via the Lie symmetry technique. First, using the three stages of symmetry reductions, we converted the generalized breaking soliton (GBS) equation into various nonlinear ordinary differential equations (NLODEs), which have the advantage of yielding a large number of exact closed-form solutions. All established closed-form wave solutions include special functional parameter solutions, as well as hyperbolic trigonometric function solutions, trigonometric function solutions, dark-bright solitons, bell-shaped profiles, periodic oscillating wave profiles, combo solitons, singular solitons, wave-wave interaction profiles, and various dynamical wave structures, which we present for the first time in this research. Eventually, the dynamical analysis of some established solutions is revealed through three-dimensional sketches via numerical simulations. Some of the new solutions are often useful and helpful for studying the nonlinear wave propagation and wave-wave interactions of shallow water waves in many new high-dimensional nonlinear evolution equations.

Inherent nonlinearities and uncertainties are inseparable parts of the real-life engineering systems, which make theoretical analyzes and control system designs more challenging. Usually, most of the physical parts in control systems such as controllers and actuators are subjected to saturation. On the other hand, uncertainties are ubiquitous in the physical systems, which demand some necessities in the design of the control system. Additionally, unreliability in communication networks is another practical constraint in multiagent systems, which is required to be considered. In this article, based on the mentioned issues, a distributed robust algorithm is proposed for the semiglobal stabilization of the voltage and frequency of isolated (islanded) ac microgrids considering parameters uncertainties, static nonlinearity of actuators, and unreliability in the communication network. In this regard, by employing an adaptive low-gain feedback protocol, the robust performance of the closed-loop system is guaranteed. Finally, to evaluate the proposed control strategy’s performance, digital time-domain simulations are carried out on a test microgrid system in MATLAB/Simulink environment, and the results are compared with several previously reported methods. Simulation results and comparison with previous works reveal the proposed method’s effectiveness and accuracy in regulating the microgrid voltage/frequency and providing accurate proportional active power sharing.

The locations of marine ranch and its surrounding islands are far away from the mainland, so the cost is too high to keep power supply by laying submarine cables from the mainland. Therefore, it is necessary to build a land-sea relay networked microgrids with high proportion of renewable energy, which is able to provide multi-energy complementary and reliable power for marine ranch and its surrounding islands. It is noteworthy that the networked microgrids is a typical cyber-physical system. In order to deeply reveal the cyber physical fusion characteristics of fishery networked microgrids, the morphology and key techniques of fishery networked microgrids are summarized from two aspects: physical system and cyber system. And the interactions of cyber and physical system are described, including the support from cyber system for the physical system as state perception and control, and the power support from physical system for cyber system. Based on the above interactions, the related research on coupling analysis of cyber-physical system is summarized. Finally, some urgently studied problems have been looked forward to in fishery networked microgrids cyber-physical system.

Schrodinger equation is an indispensable model for quantum mechanics, used for modelling several fascinating complex nonlinear physical systems, such as quantum condensates, nonlinear optics, hydrodynamics, shallow-water waves, and the harmonic oscillator. The objective of this paper is to investigate and study the exact travelling wave solutions of nonlinear triple fractional Schrodinger equations involving a modified Riemann–Liouville fractional derivative. Using the Riccati-Bernoulli Sub-ODE technique, the Backlund transformation is employed to handle the posed system. The traveling wave solutions methodology lies in converting the fractional Schrodinger equations into a nonlinear system of fractional ODEs. An infinite sequence of solutions to the fractional partial differential equations can be obtained directly through solving the resulting nonlinear fractional system. Some graphical representations of the obtained solutions after selecting suitable values for fractional values and parameters are illustrated to test accuracy and verify the power, and effectiveness of the proposed method.

The fractional Laplacian, ( − △ ) s , s ∈ ( 0 , 1 ) , appears in a wide range of physical systems, including Levy flights, some stochastic interfaces, and theoretical physics in connection to the problem of stability of the matter. In this paper, a matrix transfer technique (MTT) is employed combining with spectral/element method to solve fractional diffusion equations involving the fractional Laplacian. The convergence of the MTT method is analyzed by the abstract operator theory. Our method can be applied to solve various fractional equation involving fractional Laplacian on some complex domains. Numerical results indicate exponential convergence in the spatial discretization which is in good agreement with the theoretical analysis.

Transformers are widely used in natural language processing due to their ability to model longer-term dependencies in text. Although these models achieve state-of-the-art performance for many language related tasks, their applicability outside of the natural language processing field has been minimal. In this work, we propose the use of transformer models for the prediction of dynamical systems representative of physical phenomena. The use of Koopman based embeddings provide a unique and powerful method for projecting any dynamical system into a vector representation which can then be predicted by a transformer. The proposed model is able to accurately predict various dynamical systems and outperform classical methods that are commonly used in the scientific machine learning literature., 22 pages, 14 figures, 3 appendices

We design the controls of physical systems that are faced by uncertainties. The system dynamics are described by random hyperbolic balance laws. The control aims to steer the system to a desired state under uncertainties. We propose a control based on Lyapunov stability analysis of a suitable series expansion of the random dynamics. The control damps the impact of uncertainties exponentially fast in time. The presented approach can be applied to a large class of physical systems and random perturbations, as e.g. Gaussian processes. We illustrate the control effect on a stochastic viscoplastic material model.

This paper proposes a distributed framework for vehicle grid integration taking into account the communication and physical networks. To this end, we model the electric vehicle behaviour that includes times of departure and arrival, state of charge, required energy, and its objectives, e.g., avoid battery degradation and willingness to pay. Next, we formulate the centralised day ahead distribution market which explicitly represents the physical system, supports unbalanced three phase networks with delta and wye connections, and incorporates the charging needs of electric vehicles under a uni-and a bi-directional charging scheme. The solution of the centralised market requires knowledge of electric vehicle information in terms of desired energy, departure and arrival times that electric vehicle owners are reluctant in providing. Moreover, the computational effort required to solve the day ahead distribution market in cases of numerous electric vehicles is very intensive. As such, we propose a distributed solution of the day ahead distribution market clearing mechanism over a time-varying communication network where no private information is exchanged. We also incorporate uncertainty of electric vehicle charging needs and provide guarantees on the probability of constraint satisfaction. We illustrate the proposed vehicle grid integration framework through the 13-bus, 33-bus, and 141-bus distribution feeders.

Aspirations among human beings for living a long life with joy and comfort are escalating the demand for advanced technologies. Humans having any physical disorders wish to live a healthier life through the artificial organ transplant-ship. Here it is noteworthy that the replaced organ should behave like the original one otherwise it may affect the other organs too. It is an exaggeration to say that the usage of the computational approach in getting the solutions for real-life problems is not new. Still, the contribution of the utility of computational approaches in Bio-systems is not at par. The reason is one can’t provide multiple inputs especially in the physical systems in the industry. This leads to creating gaps in the inputs to the tools about detailed knowledge of the bio-systems. The present study reports barriers associated with the utility of the additive manufacturing process for developing the products of Bio-systems.

Multi-agent distributed optimal coordination (DOC) involves the motion conflicts of large numbers of physical systems, which leads to significant safety challenges in terms of collision avoidance. This paper studies the problem of secure DOC for multiple uncertain Euler-Lagrangian (EL) systems. The objective is to steer each EL agent to achieve the optimization task while avoiding collisions with other agents. The main challenge focuses on the co-design of optimal coordination strategy and collision avoidance mechanism. The safety barrier certificates in the closed form of path integrals are constructed which generate provably collisions-free behaviors. With convex optimization and adaptive nonlinear control, a fully distributed protocol is proposed. Based on Lyapunov method and boundedness analysis for the barrier functions, it is proved that the protocol simultaneously guarantees the global convergence and collision avoidance of the EL systems in the presence of parametric uncertainties.

Extractive fermentation is a technology used to combat product inhibition. However, there has been comparatively low adoption of this technique in industry and this can be attributed to, among other reasons, a need for novel and optimised bioreactor configurations to facilitate this unit operation. Recent research has proposed a novel Semi-Partition Bioreactor (SPB) which combines the fermentation (mixer section) and extraction (settler section) steps in one reactor thus simplifying the operation of extractive fermentation. However, while the bioreactor was shown to be feasible, an improved understanding of the reactor’s hydrodynamics is required to allow for optimised designs. This study thus focuses on the use of validated Computational Fluid Dynamics (CFD) simulations to investigate the hydrodynamics of the SPB. Key findings illustrate that, unsurprisingly, normal stirred tank reactors’ hydrodynamics are significantly changed by the introduction of a settler. Consequently, the modified hydrodynamics influence the volumetric flux between the fermentation and extraction sections. Accurate modelling of this flux gives insights into various operational choices and physical systems (for instance modifying the settler insert). Key then is accurately matching the CFD model with experimental work. For example, it has been shown that mixing time (in the settler) can be predicted to within 14.8% accuracy if the transient nature of the volumetric flux is considered as opposed to a 57% accuracy if a constant flux is assumed. Finally, a design approach for the in silico prediction of possible volumetric removal rates of top phase from the settler has been proposed. This approach would be of interest to a bioreactor engineer seeking to, for example, estimate an optimal removal rate that prevents over-accumulation of a toxic species and subsequent product inhibition.

In this chapter, we describe a simple experiment about the spin property of an electron and use it to determine what is a physical observable in quantum mechanics. We give a general description of physical systems with two distinct states. We conclude this chapter with a brief description how spin can be used in magnetic resonance imaging, a technique that is used in hospitals to reveal what is going on inside your brain.

This paper deals with the control-oriented energy-based modeling of ionic polymer metal composite patches using multiscale infinite dimensional port-Hamiltonian formulations and Lagrange multipliers. Inspired by the work of Gou Nishida et al. 2012, but considering different assumptions, this paper focuses on the constraints arising from the coupling between the polymer gel and the compliant mechanical structure of the actuator, under the quasi-static mechanical assumption for the gel, leading to a constrained port Hamiltonian system. The geometric structure of the overall system and the associated energy balance are derived. The proposed energy-based model of the IPMC actuator allows deriving controllers via energy-based control design methods with a clear physical interpretation. The proposed actuator model is further discretized in space using a structure preserving finite difference method. The Lagrange multipliers are eliminated using coordinate projections. Simulations are compared with experimental results. With proper discretization numbers, our model is consistent with the physical system. Finally, Lagrange multipliers are exploited to connect the actuator to a 2-dimensional flexible structure stemming from the modeling of a flexible endoscope.

Power hardware-in-the-loop (PHIL) simulation combines the advantages of digital simulation and physical experiment, which provides great convenience for research and verification of a complex electric system, such as the high-voltage direct current (HVdc) system. The digital interface algorithm realizes interconnection of the digital and physical system, but it is also the main factor to affect stability and accuracy of the PHIL results. In this article, the damping impedance interface algorithm is adopted to test the performance of a modular multilevel converter (MMC) connected into an HVdc system. Instead of online acquiring the exact MMC dc impedance, a simple RLC damping impedance is proposed to ensure stability and particularly, low-frequency accuracy for the PHIL system. Moreover, as testing in the laboratory does not allow hundreds of MVA-rated physical MMC, suitable scaling factors are proposed to design a scaled-down MMC prototype which presents identical dynamic characteristic with a full-scale MMC. A PHIL setup including RT-LAB real-time simulator, power amplifier, and a scaled-down MMC prototype is built and the experiments confirm the effectiveness of the proposed interface algorithm.

Computational perspectivalism has been recently proposed as an alternative to mainstream accounts of physical computation, and especially to the teleologically-based mechanistic view. It takes physical computation to be partly dependent on explanatory perspectives, and eschews appeal to teleology in helping individuate computational systems. I assess several varieties of computational perspectivalism, showing that they either collapse into existing non-perspectival views; or end up with unsatisfactory or implausible accounts of physical computation. Computational perspectivalism fails therefore to be a compelling alternative to perspective-independent theories of computation in physical systems. I conclude that a teleologically-based, non-perspectival mechanistic account of physical computation is to be preferred.