Switching Coefficient Research Articles

Secure IRS-assisted Energy Scavenging NOMA Impaired by Realistic Factors

Intelligent reflective surface (IRS)-assisted energy scavenging (ES) non-orthogonal multiple access (NOMA) with jamming (sIEnoma) is promising for high spectral efficiency, energy efficiency, information security, and communication reliability. Nonetheless, practical imperfections, such as channel state information imperfection (CSIi), hardware imperfection (HWi), fading severity, and nonlinear energy scavenging (nlES) impact scavenged energy, information security, and communication reliability of sIEnoma. Accordingly, this paper proposes performance analysis for sIEnoma under these imperfections to quickly assess its potentials without time-consuming simulations. Results show that imperfections (HWi, CSIi, fading severity, nlES) and specifications (IRS, NOMA) significantly impact performance of sIEnoma. Nevertheless, proper adoption of power splitting factor, desired data rate, HWi level, and time switching coefficient can hinder complete outage in sIEnoma. Additionally, optimal metrics are achieved by configuring system parameters properly. Moreover, the proposed sIEnoma outperforms its baseline (IRS-assisted ES orthogonal multiple access with jamming).

Non-homogeneous stochastic LQ control with regime switching and random coefficients

Non-homogeneous stochastic LQ control with regime switching and random coefficients

Adaptive Finite-Time Trajectory Tracking Control for Coaxial HAUVs Facing Uncertainties and Unknown Environmental Disturbances

In this paper, the problems of system design, dynamic modeling, and trajectory tracking control of coaxial hybrid aerial underwater vehicles (HAUVs) with time-varying model parameters and composite marine environment disturbances are investigated. It is clear that a stable transition between different media remains a challenge in the practical implementation of amphibious tasks. For HAUVs, accurate dynamic modeling to describe complex dynamic variations during vehicle takeoff from underwater to air is a huge challenge. Meanwhile, due to the rapid changes in model parameters and the external environment, vehicles are likely to fall into the sea during the cross-domain process. An integrated continuous dynamic model considering hydrodynamic changes is established by introducing a linear switching coefficient during the process of trans-medium motion. A nonsingular fast terminal sliding-mode control (NFTSMC) algorithm combined with adaptive technology is used to design the position and attitude of the controller. With no previous knowledge of external interferences and lumped uncertainties of the HAUV, the adaptive NFTSMC (ANFTSMC) algorithm achieves the control objectives; at the same time, the inherent chattering problems of sliding mode control (SMC) are weakened. The finite-time stability of the global system is proven strictly using a series of mathematical derivations based on Lyapunov theory. The effect of the controller applied is analyzed through a series of simulations with representative working conditions. The results show that the proposed ANFTSMC can realize a “seamless” air–water trans-medium process, which proves the superiority and robustness of the proposed control algorithm.

Simultaneous Wireless Information and Power Transfer in mmWave Networks Under User-Centric Base Station Clustering

User-centric base station (BS) deployment has been designed for the fifth-generation (5G) dense millimeter wave (mmWave) networks for alleviating the inter-cell interference and improving the cell-edge user experience. However, the system power consumption increases sharply with the network density. In this paper, we investigate a user-centric simultaneous wireless information and power transfer (SWIPT) mmWave system employing a time-switching protocol at users to allow both energy harvesting (EH) and data decoding. To enable user-centric BS cooperation, adaptive BS clustering model is used to determine the user’s serving cluster based on its channel condition. Considering both linear and non-linear EH models, we analyze the joint coverage, namely, the probability that the user harvests enough energy in a given time slot and receives the required data from its serving cluster. The random serving clusters and the correlation between the amount of harvested energy and received data rate make the joint coverage analysis more challenging. A tractable tight approximation of the joint coverage probability is thus derived for ultra-dense networks. A mathematical optimization model for the time switching coefficient is also developed to maximize the system joint rate and energy coverage performance. All mathematical expressions are validated by Monte-Carlo simulations. Our results show that the proposed analytical framework is accurate and efficient for the design and deployment of SWIPT-enabled user-centric mmWave networks.

Super-planckian thermal radiation in borophene sheets

Recently, borophene, a pioneering two-dimensional metal, has been attracting broad interest due to the in-plane anisotropic polaritons and large electrical tunability. Its attractive electronic and optical properties imply that borophene can find novel applications in electronics, thermionics, and photonics. Here, we study the near-field thermal radiation (NFTR) of monolayer borophene sheets; the results show that the NFTR of monolayer borophene is three-orders of magnitude larger than the black-body limit, even significantly exceeding the NFTR from traditional metals and metallic oxides with metallic behavior. An analysis of the photon tunneling coefficient and plasmon dispersion indicates that the coupling of quasi-elliptic surface plasmon polaritons (SPPs) of the borophene system extends robustly to the near- and mid-infrared frequencies, producing a broadband near-field emission from borophene. We also analyze the possibility of using the electrochemical method (that is, tuning the electron density) to modulate the NFTR between borophene sheets. The results reveal since the suppression on the wave-vector range and the anisotropy of SPPs by electron density, the switching coefficient of such electrochemical method can exceed up to 95%. Finally, the modulate effect of the mechanical decoupling on the NFTR is explored. We clarify the influence of decoupling effect at different frequencies on the mechanical thermal modulation of borophene. All in all, we explore the NFTR of the 2D borophene thoroughly for the first time, opening potential possibility for novel borophene-dependent thermal devices and thermal management.

Dissipativity-based synthesis for semi-Markovian systems with simultaneous probabilistic sensors and actuators faults: A modified event-triggered strategy

Aided by a modified event-triggered communication policy (ETCP), this article addresses the dissipativity-based control synthesis problem for semi-Markovian switching systems (SMSSs) with simultaneous multiplicative probabilistic faults on sensors and actuators modules. The resulting model under consideration is more extensive, which covers semi-Markovian switching coefficients, transmission delays, and randomly occurring sensors and actuators faults in a unified systematic analytical framework instead of investigating separately in some existing works. More specifically, the probabilistic faults are assumed to happen on both the sensors and actuators modules simultaneously, and the distortion probability for each sensor and actuator is irrelevant, which can be characterized by multiplicate mutually independent stochastic variables that obeys certain statistical features and probabilistic distribution delineate on the interval [0,✠](✠≥1). To reduce the bandwidth usage, a novel event-triggered strategy is designed. Additionally, in the light of this newly developed ETCP, and considering the effects of the signal transmission delays and multitudinous probabilistic failures, a generalized and more realistic faulty pattern for SMSSs is presented, which is more fit for real applications. Hereby, the principal superiority of the established new type faulty pattern lies in its practicality and generality, which contains some previous faulty models as special scenarios. By constructing an appropriate semi-Markovian Lyapunov functional (SMLF) together with mathematical analysis technique and matrix inequality decoupling operation, sojourn-time-dependent sufficient conditions for determining both the control gain matrices and triggered configuration coefficients are developed and formulated in terms of a group of feasible linear matrix inequalities (LMIs). Eventually, several practical examples are exploited to substantiate the validity and practicability of the developed control design methodology.

Improved fixed‐time stability results and application to synchronization of discontinuous neural networks with state‐dependent switching

AbstractIn this article, we considered the fixed‐time stability (FXT) of a type of discontinuous dynamical system. First, we introduced some new FXT stability results via variable substitutions and using inequality techniques, which are more accurately estimate the upper bound of settling time (ST). Then, based on the established results and using the differential inclusion theory, we investigated the FXT synchronization of a type of general neural networks (NNs) with state‐dependent switching coefficients and discontinuous neuron activation functions via introducing a type of discontinuous controller which is more simple compared to existing results. In addition, the estimated upper bound of ST shown to be independent to the initial values of considered drive‐response networks, and it is much smaller and nearer to the real synchronization time than those given in the previously published works. Lastly, two numerical examples are given to demonstrate the feasibility of our derived theoretical results. We believe that the results of this article can give some new insights for the FXT stabilization and FXT synchronization analysis of state‐dependent switching dynamics with or without discontinuous right‐hand sides.

Risk‐sensitive maximum principle for stochastic optimal control of mean‐field type Markov regime‐switching jump‐diffusion systems

AbstractWe consider the risk‐sensitive optimal control problem for mean‐field type Markov regime‐switching jump‐diffusion systems driven by Brownian motions and Poisson jumps with (Markovian) switching coefficients. The system is coupled with its mean‐filed term, that is, the expected value of the state process, and the objective functional is of the risk‐sensitive type. Our problem is closely related to the mean‐field type robust optimization problem for a general class of stochastic jump systems due to the inherent feature of the risk‐sensitive objective functional. By establishing the logarithmic transformations of the associated equivalent singular risk‐neutral control problem, we obtain the risk‐sensitive maximum principle type necessary and sufficient conditions for optimality, where the sufficient condition requires an additional convexity assumption. The risk‐sensitive maximum principle in this article is characterized as the variational inequality, together with the first‐ and second‐order (mean‐field type) adjoint processes as well as the auxiliary first‐order adjoint process. Unlike the risk‐neutral and mean‐field free cases, the additional adjoint equation is induced due to the mean‐field coupling term and the risk‐sensitive logarithmic transformation. We apply the risk‐sensitive maximum principle of this article to the risk‐sensitive linear‐quadratic problem, for which an explicit optimal solution is obtained.

Modeling and optimal control of fast filling process of hydrogen to fuel cell vehicle

Due to the rapid compression of hydrogen and the Joule-Thompson effect specific to hydrogen during the fast filling process, the internal temperature of the cylinder rises sharply which may lead to hidden safety hazards. In this study, a high-pressure hydrogen filling process is considered, and a simple mathematical model of a cascade storage system of a hydrogen refilling station is developed to analyze the temperature rise in hydrogen cylinders under different working conditions. The results show the pressure switching coefficient has a great impact on the filling time, and the pre-cooling of hydrogen has a significant impact on the temperature rise and the states of charge (SOC) within cylinder. Herein, a multi-objective iterative optimization algorithm is proposed to calculate the above two controllable variables (pressure switching coefficient, pre-cooling temperature of hydrogen) with the objectives of faster refueling, lower energy consumption and higher SOC within cylinders in cascade hydrogen refueling. Besides, this method could significantly decrease energy consumption, improve SOC and allow acceptable refueling time.

Spectrum sharing protocol in two-way cognitive radio networks with energy accumulation in relay node

In this paper, we consider a two-way cognitive radio network where an energy-constrained secondary transmitter (ST) first performs energy accumulation and then assists bidirectional communication for a pair of primary users (PUs) based on the principle of two-way relaying (TWR) strategy. To be specific, the ST can harvest energy from the received radio frequency (RF) signals broadcasting by both the primary users. After the residual energy at the ST is sufficient for data transmission, the ST will forward PUs’ signals by using decode-and-forward (DF) manner. In return, the ST is allowed to access the primary spectrum for its own data transmission. Moreover, according to whether the ST can correctly decode PUs’ signals or not, the ST will opportunistically switch between the silent mode and data transmission mode. A discrete Markov chain is used to simulate the charging and discharging processes of the ST’s battery. Based on this, closed-form expressions of outage probabilities for both the primary and secondary systems are derived. In addition, the optimal sub-slot switching coefficient and power allocation coefficient are determined by maximizing the spectrum efficiency of the secondary system while the spectrum efficiency of the primary system exceeding a given threshold. Numerical results not only validate the accuracy of the analytical results but also show that the proposed spectrum sharing protocol can provide a better performance in terms of energy efficiency over other similar TWR schemes.

First-Principles Investigation of Photoisomeric Switching of Vibrational Heat Current across Molecular Junctions

Photoisomeric molecules rearrange their structure when exposed to light, which alters their chemical, electronic, mechanical, as well as vibrational properties. The present study explores the possibilities to tune the thermal transport across molecular junctions by using photoisomeric molecules. The effect of isomeric switching on phonon transport through single-molecule junctions linking two macroscopic reservoirs is investigated using density-functional-theory-based tight-binding calculations and Green-function formalism. The junctions are built using azobenzene and its derivatives (azobiphenyl and azotriphenyl) that display photoisomeric behavior. Effects of system setup on the heat current and the switching coefficient are studied systematically. Dependence on the molecular species, the choice of reservoir, as well as the type of linkers that bind the molecules to the reservoir are investigated with calculating the phonon-transmission spectra and temperature-dependent thermal conductance values. The results show that thermal conductance can be altered significantly by switching the molecule from trans- to cis-configuration since all molecules yield higher conductances in trans-configurations than their cis-configurations at temperatures higher than 50 K. In the low-temperature range, results reveal considerable switching coefficients exceeding $50\mathrm{%}$. At room temperature, the switching coefficient can be as high as $20\mathrm{%}$. It is shown that the effect is robust under the variation of both the molecular species and the linkers.

Control of the two-wheeled inverted pendulum (TWIP) robot in presence of model uncertainty and motion restriction

Purpose The purpose of this paper is to improve control performance and safety of a real two-wheeled inverted pendulum (TWIP) robot by dealing with model uncertainty and motion restriction simultaneously, which can be extended to other TWIP robotic systems. Design/methodology/approach The inequality of lumped model uncertainty boundary is derived from original TWIP dynamics. Several motion restriction conditions are derived considering zero dynamics, centripedal force, ground friction condition, posture stability, control torque limitation and so on. Sliding-mode control (SMC) and model predictive control (MPC) are separately adopted to design controllers for longitudinal and rotational motion, while taking model uncertainty into account. The reference value of the moving velocity and acceleration, delivered to the designed controller, should be restricted in a specified range, limited by motion restrictions, to keep safe. Findings The cancelation of model uncertainty commonly existing in real system can improve control performance. The motion commands play an important role in maintaining safety and reliability of TWIP, which can be ensured by the proposed motion restriction to avoid potential movement failure, such as slipping, lateral tipping over because of turning and large fluctuation of body. Originality/value An inequation of lumped model uncertainty boundary incorporating comprehensive errors and uncertainties of system is derived and elaborately calculated to determine the switching coefficients of SMC. The motion restrictions for TWIP robot moving in 3D are derived and used to impose constraints on reference trajectory to avoid possible instability or failure of movement.

Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems

In this paper, we establish that for a wide class of controlled stochastic differential equations (SDEs) with stiff coefficients, the value functions of corresponding zero-sum games can be represented by a deep artificial neural network (DNN), whose complexity grows at most polynomially in both the dimension of the state equation and the reciprocal of the required accuracy. Such nonlinear stiff systems may arise, for example, from Galerkin approximations of controlled stochastic partial differential equations (SPDEs), or controlled PDEs with uncertain initial conditions and source terms. This implies that DNNs can break the curse of dimensionality in numerical approximations and optimal control of PDEs and SPDEs. The main ingredient of our proof is to construct a suitable discrete-time system to effectively approximate the evolution of the underlying stochastic dynamics. Similar ideas can also be applied to obtain expression rates of DNNs for value functions induced by stiff systems with regime switching coefficients and driven by general Lévy noise.

Estimation of jump Box–Jenkins models

Jump Box–Jenkins (BJ) models are a collection of a finite set of linear dynamical submodels in BJ form that switch over time, according to a Markov chain. This paper addresses the problem of maximum-a-posteriori estimation of jump BJ models from a given training input/output dataset. The proposed solution method estimates the coefficients of the BJ submodels, the state transition probabilities of the Markov chain regulating the switching of operating modes, and the corresponding mode sequence hidden in the dataset. In particular, the posterior distribution of all the unknown variables characterizing the jump BJ model is derived and then maximized using a coordinate ascent algorithm. The resulting estimation algorithm alternates between Gauss–Newton optimization of the coefficients of the BJ submodels, a method derived based on an instance of prediction error methods tailored to BJ models with switching coefficients, and approximated dynamic programming for optimization of the sequence of active modes. The quality of the proposed estimation approach is evaluated on a numerical example based on synthetic data and in a case study related to segmentation of honeybee dances.

Light-Sensitive Material Structure-Electrical Performance Relationship for Optical Memory Transistors Incorporating Photochromic Dihetarylethenes.

Photoswitchable organic field-effect transistors (OFETs) with embedded photochromic materials are considered as a promising platform for development of organic optical memory devices. Unfortunately, the operational mechanism of these devices and guidelines for selection of light-sensitive materials are still poorly explored. In the present work, a series of photochromic dihetarylethenes with a cyclopentenone bridge moiety were investigated as a dielectric/semiconductor interlayer in the structure of photoswitchable OFETs. It was shown that the electrical performance and stability of the devices can be tuned by variation of the substituents in the structure of the photochromic material. In particular, it was found that dihetarylethenes with donor substituents demonstrated the best light-induced switching effects (wider memory windows and higher switching coefficients) in the devices. The operation mechanism of the light-triggered memory devices was proposed based on the differential in situ Fourier transform infrared (FTIR) spectroscopy data and regression analysis of the threshold voltage-programming time experimental dependencies. The established relationships will facilitate further rational design of new photochromic materials, thus paving a way to fast and durable organic optical memories and memory transistors (memristors).

Memory devices based on novel alkyl viologen halobismuthate(iii) complexes.

Four novel halobismuthate(iii) complexes with alkyl viologen cations: (R2Viol)2[Bi2X10] (R = n-butyl, n-pentyl, X = Cl, Br) have been synthesized. Both chloride complexes revealed photochromic behavior and were successfully utilized for the fabrication of OFET-based memory devices with high switching coefficients and good write-read-erase cycling stability.

Optimum Design of Energy Harvesting Relay for Two-Way Decode-and-Forward Relay Networks Under Max–Min and Max-Sum Criterions

We study the optimum design of an energy harvesting relay for two-way decode-and-forward (DF) relay networks. In the networks, the relay harvests energy as well as decodes information using the received signal from two sources with power splitting relaying (PSR) and time switching relaying (TSR) strategies. Since the two-way relay network has two opposite traffic flows, it can be considered as a special case of multi-user systems. Applying the max–min criterion for fairness and max-sum criterion for maximum resource utilization, we optimize the operations of the energy harvesting relay. Specifically, considering the transmission rate constraints of individual hops, we derive optimum power splitting coefficients and optimum time switching coefficients, respectively, for PSR-based and TSR-based networks under both criterions, and analytically calculate the resulting maximum transmission rates. Numerical results confirm that our analyses exactly match with exhaustive search simulations. The obtained optimum coefficients are given in closed-form, therefore, they can be easily adopted in simple two-way relay networks with limited computational power such as sensor/Internet of Things (IoT) networks and can help two-way DF relay networks.

Optimum SWIPT relaying in bidirectional non‐regenerative relay networks

The authors study the optimal design of simultaneous wireless information and power transfer relaying in bidirectional non-regenerative relay networks. They consider both power splitting relaying and time switching relaying. Since there are two opposite traffic flows between two sources, they characterise the transmission rate performance by the minimum rate of two opposite traffic flows. Specifically, they first obtain closed-form expressions for the optimum power splitting and time switching coefficients to maximise the minimum transmission rate of bidirectional non-regenerative relaying. They also derive the resulting maximum transmission rate expressions. Furthermore, they consider a minimum input radio frequency (RF) power level to turn on diodes, and derive the turn-on probability of the RF energy harvesting circuit when two channels experience independent Rayleigh fading. Numerical results confirm that the authors' analyses exactly match with simulation results and that the two relaying strategies with optimum coefficients give almost the same transmission rate performance.

Robust chattering-free optimal sliding-mode control using recurrent neural networks: An H∞-based approach

In this paper, a robust and chattering-free sliding-mode control strategy using recurrent neural networks (RNNs) and H∞ approach for a class of nonlinear systems with uncertainties is proposed. The dynamic and algebraic models of the RNN are extracted based on the nominal model of the system and formulation of a quadratic programming problem. For tuning the parameters of the sliding surface, the performance index and the switching coefficient, a robust approach based on the H∞ method is developed. To this end, the control law is divided into two parts: (1) the main term, which includes the feedback error and (2) other terms, which include the network states, the reference input and its derivatives and the effects of the uncertainties. The feedback error gain is tuned by solving a linear matrix inequality. The neural optimizer determines the sliding-mode control law without being directly affected by the uncertainties. By applying the proposed method to the continuous-stirred reactor tank and the inverted pendulum problems, the performance of the proposed controller has been evaluated in terms of the tracking accuracy, elimination of the chattering, robustness against the uncertainties and feasibility of the control signals. Moreover, the results are compared with the conventional and twisting sliding-mode control methods.

Synchronizability of two neurons with switching in the coupling

Time-varying networks are prominently present in the nervous system, where the connections between neurons vary according to the activity of pre and post synapses. Using this as motivation, we here consider a system of two ephaptically coupled neurons with a periodically time-varying link between the two membrane potentials. We derive the master stability function in dependence on the membrane potential coupling strength and in dependence on the ephaptic coupling strength. We first determine coupling ranges at which the two neurons are synchronizable if the link between them is static. In doing so, we find that the ephaptic coupling has a negligible impact on synchronization, while the coupling through the membrane potential has a strong effect. We then consider switching in the coupling, determining the average synchronization error for a fixed ephaptic coupling strength and different membrane potential coupling strengths, and for various switching frequencies. We find the values of the switching coefficient that can synchronize the two neurons for each of the considered switching frequencies, and finally, we also use the basin stability method to determine the global stability of synchronization.