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Unconsciousness maintained by GABAergic anesthetics, such as propofol and sevoflurane, is characterized by slow-delta oscillations (0.3 to 4 Hz) and alpha oscillations (8 to 14 Hz) that are readily visible in the electroencephalogram. At higher doses, these slow-delta–alpha (SDA) oscillations transition into burst suppression. This is a marker of a state of profound brain inactivation during which isoelectric (flatline) periods alternate with periods of the SDA patterns present at lower doses. While the SDA and burst suppression patterns have been analyzed separately, the transition from one to the other has not. Using state–space methods, we characterize the dynamic evolution of brain activity from SDA to burst suppression and back during unconsciousness maintained with propofol or sevoflurane in volunteer subjects and surgical patients. We uncover two dynamical processes that continuously modulate the SDA oscillations: alpha-wave amplitude and slow-wave frequency modulation. We present an alpha modulation index and a slow modulation index which characterize how these processes track the transition from SDA oscillations to burst suppression and back to SDA oscillations as a function of increasing and decreasing anesthetic doses, respectively. Our biophysical model reveals that these dynamics track the combined evolution of the neurophysiological and metabolic effects of a GABAergic anesthetic on brain circuits. Our characterization of the modulatory dynamics mediated by GABAergic anesthetics offers insights into the mechanisms of these agents and strategies for monitoring and precisely controlling the level of unconsciousness in patients under general anesthesia. [ABSTRACT FROM AUTHOR]

The pitch control system has a profound impact on the development of wind energy, and yet a delay or non-minimum phase can weaken its performance. Thus, there is a strong incentive to enhance pitch control technology in order to counteract the negative effects of unidentified delays and non-minimum phase characteristics. To reduce the complexity of the parameter-tuning process and improve the performance of the system, in this paper, we propose a novel control method for wind turbine pitch angle with time delays. Specifically, the proposed control method is state-space PIDD2, which is based on internal model control (IMC) and the open-loop system step response. Then, considering the tracking, disturbance rejection and measurement noise, the proposed controller is verified through simulations. The simulation results demonstrate that the state-space PIDD2 (SS-PIDD2) can provide a trade-off between robustness, time domain performance and measurement noise attenuation and effectively improve pitch control performance in contrast to series PID and PI control methods. [ABSTRACT FROM AUTHOR]

There are few mathematical models that describe how human perceptual and affective responses evolve with time. We developed a state-space model of the interrelationships based on time-evolving perceptual and affective responses acquired by the temporal dominance (TD) method. We defined and computed the state variables that endow the system with memory using canonical variate analysis. Furthermore, we determined the model parameters on the basis of a cross-validation approach such that the observed and estimated changes in the affective responses are highly correlated. We applied this method to the TD curves of responses to eating strawberries and plum pickles, reported in our previous work. The model describes what happens during food intake in terms of a few state variables that summarize a large number of observable responses reductively, thus helping to make the perceptual and affective dynamics understandable. [ABSTRACT FROM AUTHOR]

Hybrid modeling workflows combining machine learning with mechanistic process descriptions are becoming essential tools for bioprocess digitalization. In this study, a hybrid deep modeling method with state–space reduction was developed and showcased with a P. pastoris GS115 Mut+ strain expressing a single-chain antibody fragment (scFv). Deep feedforward neural networks (FFNN) with varying depths were connected in series with bioreactor macroscopic material balance equations. The hybrid model structure was trained with a deep learning technique based on the adaptive moment estimation method (ADAM), semidirect sensitivity equations and stochastic regularization. A state–space reduction method was investigated based on a principal component analysis (PCA) of the cumulative reacted amount. Data of nine fed-batch P. pastoris 50 L cultivations served to validate the method. Hybrid deep models were developed describing process dynamics as a function of critical process parameters (CPPs). The state–space reduction method succeeded to decrease the hybrid model complexity by 60% and to improve the predictive power by 18.5% in relation to the nonreduced version. An exploratory design space analysis showed that the optimization of the feed of methanol and of inorganic elements has the potential to increase the scFv endpoint titer by 30% and 80%, respectively, in relation to the reference condition. [ABSTRACT FROM AUTHOR]

Multiple state variables governed by internal processes within the human body remain unobserved. On a number of occasions, these states are linked to point process bioelectric and biochemical observations coupled together with continuous-valued variables. These observations provide a means to estimate the latent states of interest. We develop a state-space method to estimate unobserved sympathetic arousal and energy production states from skin conductance and cortisol data respectively, comprising of a marked point process and a continuous-valued observation. The method involves Bayesian filtering applied within an expectation-maximization (EM) framework for state estimation and model parameter recovery. Results are evaluated on both simulated and experimental data. On experimental skin conductance data, high arousal levels are generally detected during cognitive stress periods and lower values are detected during relaxation. Results are also in conformity with general physiology for cortisol data. On separate experimental data, skin conductance-based estimates are validated/cross-checked with functional Near Infrared Spectroscopy features. Estimation is also performed on heart rate and skin conductance data to illustrate the method’s wider applicability. We also compare the method with earlier approaches. We show how it outperforms a previous method for cortisol-based energy estimation, and its superiority to earlier methods for estimating sympathetic arousal. The EM approach is thus able to estimate latent physiological states within the body from point process bioelectric and biochemical phenomena. The method could be applied in wearable monitoring and automated closed-loop therapy delivery for patients diagnosed with certain types of neuropsychiatric or hormone disorders.

Reducing heat losses through the building envelope is one of the most important aspects to be met if the targets set by the European Union are to be achieved. In order to obtain a more realistic energy demand, dynamic heat transfer simulations are used to calculate the energy consumption of buildings, since steady-state calculations do not take into account the thermal mass in buildings. These dynamic simulations employ methods based on analytical models since numerical models are unsuitable for longer time periods. The analytical models used herein fall into the category of conduction transfer functions (CTFs). Two methods for computing CTFs that are addressed in this research are the Laplace method and the State-Space method. The objective of this paper is to verify the efficiency of the Laplace and State-Space methods for calculating the energy demand of a building in the case of heavyweight building elements and shorter sampling times, and to provide a means for improving the algorithms used by these methods. The Laplace and State-Space method algorithms were implemented in Mathematica, and the results were compared to EnergyPlus and TRNSYS, which use similar algorithms to calculate energy demand. It was shown in this paper that for the heavyweight wall element and a time step of 0.25 h, the difference between the total energy transferred through the inner surface was about 31% for EnergyPlus and 78% for TRNSYS compared to the reference solution. For the lightweight wall element, the results were stable for the time step of 0.25 h, but for the time step of 0.1 h, the differences were 45.64% and 303% between EnergyPlus, TRNSYS and the reference solution, respectively, compared to the State-Space and Laplace methods for which the maximum difference was 12.03% with a time step of 0.1 h. While dynamic heat transfer simulations are better than calculations based on steady-state boundary conditions, they also have their limitations and could lead to unsatisfactory results for short sampling times and if not applied properly. [ABSTRACT FROM AUTHOR]

A high gain two-phase interleaved coupled inductor-based DC-DC converter step-up low input voltage sources like PV array and fuel cell in distribution generation system (DGS). The converter operates for a wide range of load changes when it is part of DGS. At light load, the converter operates in discontinuous conduction mode (DCM). A small-signal model (SSM) is required in both continuous conduction mode (CCM) and DCM to know the complete dynamic behavior of the converter. Steady-state and small-signal analysis of the two-phase interleaved coupled inductor boost converter (ICIBC) is quite complex when the converter operates in DCM. This paper presents the complete derivation of SSM of a two-phase ICIBC-based high step-up gain converter in DCM. The conditions of the converter operating in DCM are discussed. The small-signal model of two-phase ICIBC is derived using the state-space averaging method. The control-to-output voltage transfer function and steady-state voltage gain of the two-phase ICIBC are derived. When the converter operates in DCM, it eliminates the right half plane zero from the control-to-output voltage transfer function. Thus, it improves bandwidth and stability margin. Thus, single loop voltage control is sufficient to achieve regulated output voltage with faster response for all operating ranges of DCM. The laboratory prototype of two-phase ICIBC is implemented to step-up 24 V to 100 V at a power level of 300 W. The experimental results of two-phase ICIBC are obtained in DCM and CCM with step-change in load to validate the derived small-signal model. [ABSTRACT FROM AUTHOR]

With the increasing integration of new energy generation into the power system and the massive withdrawal of traditional fossil fuel generation, the power system is faced with a large number of stability problems. The phenomenon of low-frequency oscillation caused by lack of damping and moment of inertia is worth studying. In recent years, virtual synchronous generator (VSG) technique has been developed rapidly because it can provide considerable damping and moment of inertia. While improving the stability of the system, it also inevitably causes the problem of active power oscillation, especially the low mutual damping between the VSG and the power grid will make the oscillation more severe. The traditional time-domain state-space method cannot reflect the interaction among state variables and study the interaction between different nodes and branches of the power grid. In this paper, a frequency-domain method for analyzing low-frequency oscillations considering VSG parameter coupling is proposed. First, based on the rotor motion equation of the synchronous generator (SG), a second order VSG model and linearized power-frequency control loop model are established. Then, the differences and connections between the coupling of key VSG parameters and low-frequency oscillation characteristics are studied through frequency domain analysis. The path and influence mechanism of a VSG during low-frequency power grid oscillations are illustrated. Finally, the correctness of the theoretical analysis model is verified by simulation. [ABSTRACT FROM AUTHOR]

The high-order rational function provides an accurate and effective model of self-excited forces for flutter analysis of bridges. This study proposes a direct identification method for the rational function of a bridge deck, which enables high-order modeling. In this method, the single degree of freedom (SDOF) forced vibration test is first carried out on the sectional model of the bridge deck. The tested results are then processed by a harmonic function-based filter, yielding force time histories with high signal-to-noise ratios. Formulas are derived to identify parameters of the high-order rational function from these time histories. To deal with the numerical difficulties of the formulas, two practical optimal nonlinear fitting procedures were proposed, which simplify the identification but at the same time maintain a reasonable accuracy. A thin flat plate and a streamlined box section under various wind angles of attack were used as examples to elucidate the effectiveness and feasibility of the proposed identification method. Results showed that the identified high-order rational function could accurately replicate the self-excited force. In contrast, a significant deviation was observed if the first-order rational function was utilized as an alternative. Finally, the high-order rational function model identified was applied in the flutter analysis by a state-space method. The yielded critical flutter wind speed matched satisfactorily with the free-vibration wind tunnel test results, confirming the advantage of the proposed model. [ABSTRACT FROM AUTHOR]

The state-space model is a powerful statistical tool to estimate linear or non-linear discrete-time dynamic systems. This model naturally leads to the estimation problem of the time-varying parameters of the discovery-time demographic version of the susceptible-infected-recovered (SIR) model that we consider. In this paper, we consider computational methods to perform Bayesian inference on state-space models for analysing time-series data. We compare the three popular Bayesian computational methods for state-space models: the adaptive Metropolis-within-Gibbs algorithm, Liu and West's algorithm and variational approximation method based on Gaussian distributions. The performances of the three methods are compared based on synthetic datasets. Furthermore, we analyse the trend of the spread of COVID-19 in South Korea to point out the limitations of existing methods and derive meaningful results. [ABSTRACT FROM AUTHOR]

Population and community ecology traditionally has a very strong theoretical foundation with well‐known dynamical models, such as the logistic and its variations, and many modifications of the classical Lotka–Volterra predator–prey and interspecific competition models. More and more, these classical models are being confronted with data via fitting to empirical time series for purposes of projections or for estimating model parameters of interest. However, using statistical models to fit theoretical models to data is far from trivial, especially for time series data where subsequent measurements are not independent. This raises the question of whether statistical inferences using pure observation error models, such as simple (non‐)linear regressions, are biased, and whether more elaborate process error models or state‐space models have to be used to address this complexity. In order to help empiricists, especially researchers working with experimental laboratory populations in micro‐ and mesocosms, make informed decisions about the statistical formalism to use, we here compare different error structures one could use when fitting classical deterministic ordinary differential equation (ODE) models to empirical data. We consider a large range of biological scenarios and theoretical models, from single species to community dynamics and trophic interactions. In order to compare the performance of different error structure models, we use both realistically simulated data and empirical data from microcosms in a Bayesian framework. We find that many model parameters can be estimated precisely with an appropriate choice of error structure using pure observation error or state‐space models, if observation errors are not too high. However, Allee effect models are typically hard to identify and state‐space models should be preferred when model complexity increases. Our work shows that, at least in the context of low environmental stochasticity and high quality observations, deterministic models can be used to describe stochastic population dynamics that include process variability and observation error. We discuss when more complex state‐space model formulations may be required for obtaining accurate parameter estimates. Finally, we provide a comprehensive tutorial for fitting these models in R. [ABSTRACT FROM AUTHOR]

Bias accuracy of micromachined gyroscope is deteriorated severely when suffering from unknown environmental disturbances (UD), such as temperature, vibration, and shock. In this article, gyros array using a two-stages UD-decoupled fusion strategy is for the first time proposed to maintain high-accuracy bias under unknown disturbances. In detail, the evolvement model of the individual gyroscope among four-gyros array is first deduced to decouple UD. Then, local estimator is devised to simultaneously derive the estimates of the state, the bias, and the UD of each gyro among the array in the first stage, respectively. Further, due to the nonoptimal local state estimates caused by the inherent response difference between each gyro, the global state fusion estimator weighted by dynamic coefficients is devised to achieve the uniformly optimal state estimate of gyros array in the second stage. The experimental test shows that RMSE of the estimate based on the proposed method is evaluated as 0.13°/s, which is reduced by 92.4%, 87%, 82.4%, and 51.9% compared with kalman filter (KF), internal model approach (IMA), distributed optimal linear fusion estimator (DOLFE), and minimal learning parameter-neural network (MLP-NN), respectively. The tested scale factor of the array is calibrated as 5.9 mv/°/s and the corresponding bias stability is calculated as 0.019°/s, which is improved by 68.8%, 54.8%, 34.5%, and 17.4% compared with KF, IMA, DOLFE, and MLP-NN, respectively. Not only is bias accuracy under UD not deteriorated, on the contrary, the accuracy is improved significantly by 3.7 times compared with that without UD. The proposed array-based UD-decoupled method paves an effective path to apply microgyro in actual environment engineering. [ABSTRACT FROM AUTHOR]

Uncertainties and disturbances in traffic flows will result in performance degradation and even the critical instability of automated freeway systems. To handle these issues, a multivariable model predictive controller is proposed in this article, taking the speed and density of traffic flows as the control objectives and accounting for the uncertainty caused by ramps. Specifically, a state-space macroscopic traffic flow model is established based on a macroscopic traffic flow model. Then, a multivariable model predictive controller is developed based on the state-space model, and a sequence quadratic program algorithm is designed to find the optimal inputs. Finally, extensive simulations and comparisons are conducted. The results from the simulations verify that the proposed controller can make the density and speed of traffic flows approach a desired value more quickly and without chattering. In addition, the controller can effectively avoid traffic congestion and has better stability in the presence of disturbances. [ABSTRACT FROM AUTHOR]

This article proposes a cyberattack-resilient secondary frequency control (SFC) scheme for a stand-alone microgrid (MG) system. The proposed scheme is based on a hybrid system that employs an unknown input observer for state estimation, cyberattack detection and reconstruction, and a Type-2 fuzzy logic system for minimization of frequency deviation that may be caused by cyberattacks on the MG system. A cyberattack on the MG’s frequency measurement disrupts the operation of the SFC system and eventually compromises the frequency stability of the MG. The proposed approach shows an accurate estimation of the MG states and maintains the frequency of the MG within operating limits during various cyberattack scenarios. The practicability of the proposed hybrid scheme is validated using the Speedgoat real-time digital system simulator. [ABSTRACT FROM AUTHOR]

In the context of "TO CHAIR" project, this work aims to improve the accuracy of short-term forecasts of maximum air temperature obtained from the https://weatherstack.com/ website. The proposed methodology is based on a state-space representation that incorporates the latent process, the state, which is estimated recursively using the Kalman filter. The proposed model linearly and stochastically relates the forecasts from the website (as a covariate) to the observations of the maximum temperature recorded at the study site. The specification of the state-space model is performed using the maximum likelihood method under the assumption of normality of errors, where empirical confidence intervals are presented. In addition, this work also presents a treatment of outliers based on the ratios between the observed maximum temperature and the website forecasts. [ABSTRACT FROM AUTHOR]

Since the coupling relationship of excitation sources is complicated, meantime the motor torque changes quickly under the acceleration condition, the problem of torsional vibration is prominent. This paper studies an active damping control (ADC) strategy for a parallel hybrid electric vehicle (HEV) under acceleration condition. Primarily, a full-order dynamic model is built, and the corresponding motion equations are derived. Moreover, a controller design–oriented model is established based on model reduction algorithm. Furthermore, a method that considers time delay characteristics of actuator based on model predictive control (MPC) theory is proposed to solve the torsional vibration problem. The controller handles delay characteristics of an actuator by state-space reorganization method, and the optimal control sequence is obtained by solving the objective function. Finally, the controller is tested using Simulink simulation and hardware-in-loop simulation platform, which mainly includes the verification of model reduction and vibration damping effect. The results show that simplified third-order model has a good consistency with the original full-order model in time and frequency domain. Meanwhile, the designed controller has a considerable damping effect and ensures the comfort performance of the vehicle. This study provides an important reference for vibration control of the hybrid powertrain. [ABSTRACT FROM AUTHOR]

In the design of frames and trusses, the relationship between the structural form, the construction sequence, and the structural behavior during construction is rarely systematically considered. This paper proposes a method to systematically consider constructability in design, specifically focusing on minimizing the maximum displacement or stress during construction. The paper presents the formulation of the optimal assembly sequencing problem as a state-space search with a unique design of the search heuristic and constraint bounding schemes. It proposes three variants of heuristic search algorithms for finding a feasible sequence, a diverse set of feasible sequences, and an optimal sequence, respectively. These algorithms are tested on a case study structure to showcase their ability to navigate the combinatorial space of assembly sequences, and provide means for designers to incorporate sequence-related performance into conceptual structural design. • A constructability score is formulated for scaffold-free assembly of bar structures with rigid connections. • The displacement and stress profiles of the sequence found by the search algorithm are used to formulate the constructability score. • Three variants of greedy backtracking search algorithms are proposed to compute feasible, diverse, and optimal sequences, respectively. • The constructability score can be used to inform design decisions on a catalog of design options. [ABSTRACT FROM AUTHOR]

Objective: Epilepsy monitoring unit (EMU) admissions are critical for presurgical evaluation of drug‐resistant epilepsy but may be nondiagnostic if an insufficient number of seizures are recorded. Seizure forecasting algorithms have shown promise for estimating the likelihood of seizures as a binary event in individual patients, but methods to predict how many seizures will occur remain elusive. Such methods could increase the diagnostic yield of EMU admissions and help patients mitigate seizure‐related morbidity. Here, we evaluated the performance of a state‐space method that uses prior seizure count data to predict future counts. Methods: A Bayesian negative‐binomial dynamic linear model (DLM) was developed to forecast daily electrographic seizure counts in 19 patients implanted with a responsive neurostimulation (RNS) device. Holdout validation was used to evaluate performance in predicting the number of electrographic seizures for forecast horizons ranging 1–7 days ahead. Results: One‐day‐ahead prediction of the number of electrographic seizures using a negative‐binomial DLM resulted in improvement over chance in 73.1% of time segments compared to a random chance forecaster and remained >50% for forecast horizons of up to 7 days. Superior performance (mean error =.99) was obtained in predicting the number of electrographic seizures in the next day compared to three traditional methods for count forecasting (integer‐valued generalized autoregressive conditional heteroskedasticity model or INGARCH, 1.10; Croston, 1.06; generalized linear autoregressive moving average model or GLARMA, 2.00). Number of electrographic seizures in the preceding day and laterality of electrographic pattern detections had highest predictive value, with greater number of electrographic seizures and RNS magnet swipes in the preceding day associated with a higher number of electrographic seizures the next day. Significance: This study demonstrates that DLMs can predict the number of electrographic seizures a patient will experience days in advance with above chance accuracy. This study represents an important step toward the translation of seizure forecasting methods into the optimization of EMU admissions. [ABSTRACT FROM AUTHOR]

The multivariate autoregressive (MVAR) model is widely used in describing the dynamics of nonlinear systems, in which the estimates of model parameters and underlying states can be achieved by dual extended Kalman filter (DEKF). However, when the measurements are corrupted by complicated non-Gaussian noises, the DEKF based on the minimum mean-square error (MMSE) criterion may provide biased estimates. In the present article, we develop a novel dual Kalman-type filter, referred to as DEKF under minimum error entropy (MEE) with fiducial points (MEEFs-DEKF) to deal with the non-Gaussian noises. First, the equivalent state-space model and parameter-space model are presented based on the MVAR model. Then, an optimality criterion based on MEE with fiducial points (MEEFs) is applied in the batch-mode regression equation to improve the robustness. Finally, a fixed-point iteration algorithm gives the posterior estimates of state and parameter. Simulation results confirm that the proposed MEEF-DEKF can achieve excellent performance in various noises with different distributions, especially in heavy-tailed and multimodal noises. [ABSTRACT FROM AUTHOR]

The control synchronization of multiple electrohydraulic actuators (MEHAs) is initially discussed to ensure the consensus of every electrohydraulic actuator (EHA) with three-order isomorphic dynamics. First, the EHA model is linearized using the Lie derivative method to obtain the state-space model of MEHAs. Then, the disturbance observer is used to estimate and compensate for the unknown external load caused by the driving force of a motion plant. Via the Lyapunov technique, this protocol asymptotically achieves consensus to a zero neighborhood with the ultimate boundaries of the MEHAs' state errors. The effectiveness of the synchronous control protocol is verified by both simulation and experimental benches with two-node EHAs. [ABSTRACT FROM AUTHOR]

The processes in the Cook converter with an isoted transformer have been studied using the averaging method based on Lagrange's theorems in the mode of continuous currents and voltages in accumulating reactive elements. A mathematical model of the device based on relative variables is developed. Based on the developed model, analytical relations were obtained for the calculation of current ripple in the input and output chokes, as well as the voltage in the storage capacitors. Analytical dependences have been developed to determine the maximum values of currents and voltages in the switching elements of the converter in the entire switching range, which allows to reasonably select these elements of the device with acceptable parameters at the design stage. It is shown that neglecting the leakage inductance of the isolated transformer in the Cook converter when using expressions based on the developed mathematical model within certain limits of the transformation coefficient greatly simplifies their analytical appearance and use and does not lead to significant (more than 4-5%) deviations in calculation accuracy. References 10, figures 3. [ABSTRACT FROM AUTHOR]

As an important form of distributed renewable energy utilization and consumption, the multi-parallel inverter microgrid system works in both an isolated and grid-connected operation mode. Secondary-frequency and voltage-regulation control are very important in solving problems that appears in these systems, such as the distributed secondary-frequency regulation real-time scheme, voltage and reactive power balancing, and the secondary-frequency regulation control under the disturbances and unbalanced conditions of a microgrid system. This paper introduces key technologies related to these issues, such as the consensus algorithm and event-triggered technique, the dynamic and adaptive virtual impedance technique, and the robust and self-anti-disturbance control technique. Research and design methods such as small-signal state-space analysis, the Lyapunov function design method, the impedance analysis method, μ-synthesis design, and the LMI matrix design method are adopted to solve the issues in secondary-frequency regulation and voltage regulation. As the number of inverters increases, the structure of the microgrid becomes more and more complex. Suggestions and prospects for future research are provided to realize control with low-communication technology and a distributed scheme. Finally, for the case study, the droop-control model and primary frequency/voltage deviation of a multi-parallel inverter microgrid system is analyzed, and a state-space model of a multi-parallel inverter microgrid system with a droop-control loop is established. Then, the quantitative relationship between the primary frequency/voltage deviation and the active and reactive power output in the system is discussed. The methods and problems of centralized and decentralized secondary-frequency regulation methods, secondary-frequency regulation methods based on a consensus algorithm and an event-triggered mechanism, reactive power and voltage equalization, power distribution, and small-signal stability of the multiple parallel inverter microgrid system regarding the virtual impedance loop are analyzed. [ABSTRACT FROM AUTHOR]

We propose a new technique for performing state-space exploration of closed-loop control systems with neural network feedback controllers. Our approach involves approximating the sensitivity of the trajectories of the closed-loop dynamics. Using such an approximator and the system simulator, we present a guided state-space exploration method that can generate trajectories visiting the neighborhood of a target state at a specified time. We present a theoretical framework which establishes that our method will produce a sequence of trajectories that will reach a suitable neighborhood of the target state. We provide a thorough evaluation of our approach on various systems with neural network feedback controllers of different configurations. We outperform earlier state-space exploration techniques and achieve significant improvement in both the quality (explainability) and performance (convergence rate). Finally, we adopt our algorithm for the falsification of a class of temporal logic specification, assess its performance, and show its potential in supplementing existing falsification algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel state-space model to represent the behavior of sum-product algorithm (SPA) in the vicinity of a trapping set (TS) of a low-density parity-check (LDPC) code over the additive white Gaussian noise (AWGN) channel in the error floor region. The proposed model takes into account the non-linear behavior of SPA in the initial iterations using a quadratic approximation, and dynamically adjusts the operating point of the model in accordance to the statistical properties of TS messages. This is in contrast to the existing linear state-space models which linearly approximate the non-linear behavior of SPA at around the operating point of zero in all iterations. Simulation results are provided to demonstrate the higher accuracy of the proposed model in estimating the error floor of LDPC codes compared to the linear state-space model. We also make connections to the semi-analytical code-dependent technique proposed by Richardson for the error floor estimation of LDPC codes, and demonstrate that the proposed state-space model not only is a fully-analytical code-independent counterpart to that method, but also has less complexity and, in some cases, more accuracy. [ABSTRACT FROM AUTHOR]

This article investigates a deterministic sea wave prediction-based noncausal control scheme for the launch and recovery (L&R) from a mother ship of small rigid-hulled inflatable boats (RHIBs) for maritime rescue missions. The proposed control scheme achieves an automatic hoisting process ensuring that no collisions occur between the RHIB and mothership hull by using the cable tension force as the manipulated control input. A state-space model of the L&R system is established for the first time where the wave forces and external disturbances such as wind acting on both the mothership and the small boat are fully considered. A fast and safe recovery is ensured by a fixed-time convergent sliding-mode controller, which shortens the cable length to a target value with zero terminal velocity at a predefined time instant subject to unknown disturbances and model mismatches. Since the overall dynamics of the swing angle is underactuated, a feasibility check is proposed to avoid collisions between two vessels and overlarge angular velocities by determining a proper time instant to initiate the hoisting process. To cope with the model mismatch and the external disturbance, the constraints on the swing angle and angular velocity are tightened to ensure safety. The stability of the proposed controller is proven and details of the feasibility check are given. The fidelity of the model and the effectiveness of the proposed scheme are demonstrated in simulation where a realistic sea wave is applied. [ABSTRACT FROM AUTHOR]

In supply chain management, decision support systems and time series forecasting play an essential role. The accuracy of time-series predictions is critical for the performance optimization of every supply chain. This article suggests a method based on state-space modelling (SSM) for structured time series forecasting. Technology and advance implementations of decision support systems (DSS) have improved considerably. DSS has been used as a more restricted functionality of the database, modelling, and user interface, although technical advances made DSS even more effective. Web development has facilitated inter-organizational decision-making support systems and has resulted in many innovative implementations of current technology and many new decision-making technologies. The study of multiple configurations shows that the SSM and DSS are ideal for solving the problem being studied; in particular, the DSS guarantees appropriate prediction errors and a correct computational effort to provide adequate customer order plans. [ABSTRACT FROM AUTHOR]

This paper communicates an analytical study on computing the natural frequencies and in-plane deflections caused by static forces in the panel walls using Euler-Bernoulli, Timoshenko, Timoshenko and Goodier, Couple-stress, and Micropolar-Cosserat theory. The study highlights the formulation of the transfer matrix via the state-space method in the spatial domain; from coupled governing equations of motion that arises from the Micropolar-Cosserat theory. This theory captures the novel curvature of edges and moments of the panels at energy density level due to its unique feature of asymmetric shear stresses; that emphasizes the loss of ellipticity of governing equations. The analytical solution of the Micropolar-Cosserat theory yield appropriate results compared to plane-stress simulation of the panels using finite element analysis. [ABSTRACT FROM AUTHOR]

Since the Gaussian-inverse Wishart hierarchical form has similar properties to Student’s $t$ distribution, we name it generalized $t$ distribution in this article. Based on this, a robust generalized $t$ distribution-based Kalman filter (GTKF) is proposed for state-space models that are eroded by state and measurement outliers. Different from the existing algorithms, the state transition and measurement likelihood densities are directly modeled as generalized $t$ distributions by employing the one-step smoothing strategy.An analytical closed-form solution can be obtained through the variational inference approach. Moreover, two variants of the proposed GTKF are also presented to apply to different engineering scenarios. Simulation and experimental examples demonstrate that the proposed GTKFs yield improved robustness over the existing algorithms. [ABSTRACT FROM AUTHOR]

The periodic characteristic of modular multilevel converter (MMC), which originates from the multi-harmonic couplings among internal dynamics, results in challenges to develop the linear time-invariant (LTI) model of MMC. The linearization of MMC is inherently around time-periodic equilibrium trajectory. In this paper, two different methods to assess small-signal stability of MMC are compared. Both are based on the linear time-periodic (LTP) systems theory, but one is in the time domain (TD) and the other is in the frequency domain (FD). The LTP model of MMC is derived from the linearized arm averaged model. Then, an LTI model for the LTP system is constructed in the FD through the harmonic state-space method. Small-signal stability analysis based on Floquet theory and generalized Nyquist criterion, that accompany TD and FD, are introduced and compared. It is demonstrated that models developed in both domains can effectively capture the small-signal behavior of MMC. Third and higher-order harmonic components of the states have significant effect on the impedance-based stability analysis of MMC around mid-frequency range. Analytical results are validated by electromagnetic transient simulations based on the MMC aggregated model. [ABSTRACT FROM AUTHOR]

The line parameters of power distribution systems stored in the dispatching center may have large errors due to the line aging and system operational dynamics. Such line parameter errors may directly affect the state estimation accuracy, and the advanced management functionalities rely on these parameters. This paper proposes an augmented state estimation method of erroneous distribution line parameters considering the presence of phasor measurement units (PMUs). The augmented state-space models under single and multiple measurement snapshots are established separately by combining the parameter state equation with the state-space model of the distribution network. The augmented state estimation based on the improved adaptive unscented Kalman filter (IAUKF) is developed. The proposed method can effectively perceive the process noise statistical parameter of the erroneous parameters during the estimation to accurately estimate line parameters. The proposed solution is validated through simulation experiments using the IEEE 33-bus system and the numerical results demonstrated its accurate parameter estimation performance.Also, the scalability of the proposed algorithm is demonstrated based on the simulation of the 118-bus system with distributed generators. [ABSTRACT FROM AUTHOR]

Abstract Unified modelling, analysis, and reinforced sliding‐mode design of a high‐order buck/boost converter are proposed for DC energy systems. The high voltage gain with low current stress on the converter’s circuit components will noticeably improve its performance in DC energy systems. The mathematical model of this multimode converter is established by deploying the averaging state‐space modelling approach and a duty‐ratio constraint‐deriving method. According to the unified multimode model, a reinforced sliding‐mode controller is proposed for this converter, and the sliding surface is designed using the estimated load information. A complete stability analysis of the reinforced sliding‐mode regulated converter system is also carried out. In addition, a hardware‐based comparison study involving the proposed controller and an existing robust sliding‐mode controller is provided to validate the effectiveness of the unified workflow involving modelling, analysis, and regulation design.

Mixed-integer second-order cone programs (MISOCPs) form a novel class of mixed-integer convex programs, which can be solved very efficiently as a result of the recent advances in optimization solvers. This paper shows how various performance metrics of M/G/1 queues can be modeled by different MISOCPs. To motivate the reformulation method, it is first applied to a challenging stochastic location problem with congestion, which is broadly used to design socially optimal service systems. Three different MISOCPs are developed and compared on different sets of benchmark test problems. The new formulations efficiently solve very large-size test problems that cannot be solved by the two existing methods developed based on linear programming within reasonable time. The superiority of the conic reformulation method is next shown over a state-space decomposition method recently used to solve an assignment problem in queueing systems. Finally, the general applicability of the method is shown for similar optimization problems that use queue-theoretic performance measures to address customer satisfaction and service quality. [ABSTRACT FROM AUTHOR]

In this work, we propose a robust cooperative distributed MPC based on a multi-model approach. A new method for obtaining a state-space model in the velocity-form of inputs is presented. This model is suitable for predicting steady-state outputs within an offset-free setpoint tracking framework in which a terminal constraint is relaxed to guarantee problem feasibility under any setpoint change. The proposed strategy also deals with the zone control problem, which is widely applied in the process industry. Moreover, an alternative algorithm is proposed in order to improve convergence over iterations. Recursive feasibility, convergence and stability properties are proved. Two numerical examples are provided with the application of the proposed algorithms. • A robust cooperative distributed MPC based on a multi-model approach is presented. • The proposed strategy is suitable for offset-free setpoint tracking and zone control. • An alternative algorithm to improve convergence over iterations is proposed. • Recursive feasibility, convergence and stability properties are proved. [ABSTRACT FROM AUTHOR]

Sample clock offset must be estimated in order to correctly demodulate an OFDM signal. Estimates are also required in passive radar processing, to produce a reference signal, which is properly matched to the transmission. A recently proposed clock offset estimation method for passive radar is found to be overly sensitive to measurement noise. However, the radar ambiguity surface is found to be unaffected by the specific errors engendered by this noise. The reasons for this are explored. An updated method based on state-space modeling and Kalman smoothing is proposed. This is found to restore clock offset estimation performance in cases of higher noise, achieving this with low processing latency. These improvements enable the method to be employed for other applications requiring high quality, time-critical clock offset estimation. [ABSTRACT FROM AUTHOR]

Purpose: Providing accurate gradient currents is challenging due to the gradient chain nonlinearities, arising from gradient power amplifiers and power supply stages. This work introduces a new characterization approach that takes the amplifier and power supply into account, resulting in a nonlinear model that compensates for the current droop. Methods: The gradient power amplifier and power supply stage were characterized by a modified state‐space averaging technique. The resulting nonlinear model was inverted and used in feedforward to control the gradient coil current. A custom‐built two‐channel z‐gradient coil was driven by high‐switching (1 MHz), low‐cost amplifiers (<$200) using linear and nonlinear controllers. High‐resolution (<80 ps) pulse‐width‐modulation signals were used to drive the amplifiers. MRI experiments were performed to validate the nonlinear controller's effectiveness. Results: The simulation results validated the functionality of the state‐space averaging method in characterizing the gradient system. The performance of linear and nonlinear controllers in generating a trapezoidal current waveform was compared in simulations and experiments. The integral errors between the desired waveform and waveforms generated by linear and nonlinear controllers were 1.9% and 0.13%, respectively, confirming the capability of the nonlinear controller to compensate for the current droop. Phantom images validated the nonlinear controller's ability to correct droop‐induced distortions. Conclusion: Benchtop measurements and MRI experiments demonstrated that the proposed nonlinear characterization and digitally implemented feedforward controller could drive gradient coils with droop‐free current waveforms (without a feedback loop). In experiments, the nonlinear controller outperformed the linear controller by a 14‐fold reduction in the integral error of a test waveform. [ABSTRACT FROM AUTHOR]

Low-voltage multiterminal dc system is one of the important forms in distribution network. Its stability issue has drawn worldwide attention for the characteristics of low inertia and weak damping. This article aims to analyze the interactive behavior between the voltage source converter (VSC) and the oscillation mode of the dc network. First, this article proposed a modeling method for dc voltage control loop considering the time-lag effect of inductor energy storage behavior on the ac side of the VSC. Based on this model, the interactive behavior between VSC and dc network, including the influence of droop coefficients and load power under dc voltage–ac power (Vdc–Pac) droop control mode and under dc voltage–dc power (Vdc–Pdc) droop control mode, is studied. Then, the overall state-space model of the system is established. Furthermore, the stability of a low-voltage three-terminal dc system under different control modes and parameters is compared and analyzed in detail. Finally, the corresponding simulation and experimental verification validate the theoretical analysis. The research results show that under the same capacity and control parameters, Vdc–Pac mode can better improve the capability of power regulation and system stability. This research can provide an effective method for system control modes design. [ABSTRACT FROM AUTHOR]

Growth hormone (GH) is secreted by cells in the anterior pituitary on two time scales: discrete pulses over minutes that occur within a 24-hr pattern. Secretion reflects the balance of stimulatory and inhibitory inputs from the hypothalamus and is influenced by gonadal steroids, stress, nutrition, and sleep/wake states. We propose a novel approach for the analysis of GH data and use this approach to quantify (i) the timing, amplitude and the number of GH pulses and (ii) GH infusion, clearance and basal secretion (i.e., time invariant) rates, using serum GH sampled every 10 minutes during an 8-hour sleep study in 18 adolescents. In our method, we approximate hormonal secretory events by deconvolving GH data via a two-step coordinate descent approach. The first step utilizes a sparse-recovery approach to estimate the timing and amplitude of GH secretory events. The second step estimates physiological parameters. Our method identifies the timing and amplitude of GH pulses and system parameters from experimental and simulated data, with a median ${R^2}$ R 2 of 0.93, among experimental data. Recovering GH pulses and model parameters using this approach may improve the quantification of GH parameters under different physiological and pathological conditions and the design and monitoring of interventions. [ABSTRACT FROM AUTHOR]

This paper investigates a new wave energy converter (WEC) control problem, which is the energy maximization subject to sensor and actuator faults. Unexpected deviation of system variables from standard conditions, defined as a fault, degrades the control performance and even introduces damages breaking down the whole system. Fault detection for wave energy converters is therefore of great importance in maintaining the high reliability of the system. This paper presents a robust fault diagnosis approach effectively detecting sensor and actuator faults in real-time. A compensator is then designed to minimize the influence from faults and maintain the control performance. A non-causal linear optimal control is applied to maximize the energy output, in which the future excitation force is incorporated to determine the current control action. This approach can also be straightforwardly applied to other control methods. The parameters of the proposed fault detection method and fault-tolerant control method can be calculated off-line, which enhances the real-time implementation with a low computational burden. A realistic sea wave collected from the coast of Cornwall, U.K. is used to demonstrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Cylindrical structures, such as pipelines in power plants and novel energy-harvesting devices that combine flexible piezoelectric/piezomagnetic layers with other materials, are frequently exposed to moving heat fluxes. Understanding the physics of these structures can provide potential solutions for stress management and energy harvesting. This study investigates, for the first time, the effect of thermal wave propagation on the transient fully coupled magneto-electro-thermoelastic (METE) response of functionally graded porous (FGP) multiferroic cylindrical shells subjected to a partially distributed moving heat flux. The structure is supported by a distributed viscoelastic Winkler-Pasternak foundation, and its mechanical and electromagnetic boundary conditions are considered to be simply supported and suitably grounded. This approach is innovative as it represents the first extension of the three-dimensional (3D) Lord-Shulman coupled thermoelasticity theory to specifically address multiphysics materials. In this updated framework, the governing equations for the motion of each layer are derived following an orthotropic laminated model. To solve the problem, the state-space technique and the mathematical model of the transfer matrix are employed. The study evaluates the structure's vibrational behavior by examining four models of porosity distributions within the thermoelastic layer: symmetric, stiff non-symmetric, soft non-symmetric, and uniform. The key parameters of the dynamic response are calculated using Durbin's numerical Laplace inversion algorithm. Subsequently, to validate the proposed model, the results are compared with the findings obtained by other researchers. Comprehensive numerical results concerning temporal and spatial variations of temperature, transverse displacement, and stress components are presented for various influencing parameters such as volume fraction index, open or closed-circuit conditions, heat flux speed, porosity, and smart layer thickness. [Display omitted] • Transient fully coupled thermoelastic response of the porous multiferroic cylinders under a moving heat flux is investigated. • Lord Shulman's theory is extended to show wave-like thermal propagation in multiphysics materials for the first time. • Using Durbin's Laplace inversion algorithm, a state-space method is offered based on piezo-magneto-thermo-elasticity theory. • Four models of porosity distributions to assess the structure's vibrational behavior are studied. • The computational framework leads to efficient design and control of hybrid and smart structures under thermal stress. [ABSTRACT FROM AUTHOR]

Composite linings exhibit excellent properties such as crack resistance and high load-bearing capacity, making them suitable for tunnel support in complex soil environments. However, current research is primarily based on the one-dimensional curved Euler beam model, which struggles to consider complex shear deformation and interface conditions in multi-layer lining structures. Therefore, this paper presents, for the first time, a two-dimensional model for analyzing composite linings with arbitrary multi-layers and adopts both displacement and stress vectors as basic variables to give accurate shear deformation and inter-laminar condition simulations. By employing equivalent materials for joints and interfaces and finite element discretization in the circumferential direction, a mixed finite element method with state-space transfer equation is established. Combined with the Differential Quadrature Method, a stable solution method for the control equation under various boundary conditions is provided. Comparisons with numerical solutions in several examples validate the accuracy of this method. Parameter analysis indicates that composite linings significantly enhance the continuity and smoothness of deformation on the inner side of the lining, and by adjusting the bending stiffness of the joints, two distinct overall bending modes of the lining can be activated. This two-dimensional analysis method offers a flexible and accurate approach for the study of multi-layer composite linings, serving as a benchmark for various future studies. • A two-dimensional model and a mixed finite element method are established for the analysis of composite linings. • This method is capable of dealing with both jointed segmental linings and partially-interacting double-layer linings. • A DQM based solution enables this method to deal with nonuniform material distributions and complex boundary conditions. • Parametric study reveals two deformation modes in double-layer linings under different bending stiffnesses of joints. [ABSTRACT FROM AUTHOR]

Spin Exchange Relaxation-Free Co-Magnetometer (SERFCM) possesses ultra-sensitive rotation measurement capabilities, offering broad application prospects in inertial navigation and fundamental physics research. Precision of SERFCM in rotation measurements is directly influenced by its ability to suppress magnetic noise. In this study, a state-space method is used to establish a SERFCM response model, considering the effect of pump light wavelength. An analysis is conducted to assess the effect of pump light wavelength detuning on the ability to suppress magnetic noise of SERFCM. Theoretical results indicate that detuned pump light wavelength can effectively reduce the magnetic field gradient generated by atomic polarization, thereby improving overall system performance. Experimental validation demonstrates that a slight detuning of the pump light wavelength results in a 21% improvement in the ability to suppress low-frequency magnetic fields. [Display omitted] • Establish a SERFCM response model considering the effect of pump light wavelength. • Effect of pump light wavelength on magnetic field gradient is analyzed and proved. • The low-frequency magnetic field suppression ability improves by 21%. [ABSTRACT FROM AUTHOR]

In the spin-exchange relaxation-free (SERF) comagnetometer, the atomic polarization gradient caused by light absorption can cause significant but unexpected nuclear spin transverse relaxation, which affects the system's accuracy and sensitivity. This paper proposes the SERF comagnetometer configuration based on optical compensation to suppress polarization gradient in the vapor cell. The influence of polarization gradient on the system's magnetic field suppression ability is analyzed by the state-space method. In addition, the atomic polarization distribution simulation and the performance testing between the traditional SERF comagnetometer and the proposed configuration are conducted. Compared to traditional configuration, the polarization gradient relaxation of the proposed configuration is effectively suppressed by 62%, the magnetic field suppression ability of the system is improved by 51%, and the rotation sensitivity is increased by 25.4%. This work investigates the influence of polarization gradient on system characteristics and proposes a comagnetometer configuration to suppress polarization gradient, enhancing the system's magnetic field suppression ability and improving sensitivity. The demonstration of the polarization gradient suppression method is of significance for improving the performance of other inertial measurement devices such as nuclear magnetic resonance gyroscopes. [Display omitted] • A uniform atomic comagnetometer based on optical compensation is proposed. • The influence of polarization gradient on the system is analyzed. • The atomic spatial distribution model of the proposed configuration is established. • The polarization gradient relaxation is suppressed by 62%. • A rapid polarization confirmation method is proposed for performance comparison. [ABSTRACT FROM AUTHOR]

A comprehensive dynamic model of the three-phase grid-connected quasi Z-Source inverter (qZSI) with LCL filter is presented based on the generalized state-space averaging (GSSA) method. The developed model mathematically describes (i) the dynamic interaction between DC and AC side variables of converter and (ii) good dynamical precision that ensures a good description of the input–output dynamics, as well as the internal. Then, based on the developed model, a cascaded PI controller by nesting multi-control loops to regulate DC side capacitor voltage and inductor current is proposed. The PI controllers determine the grid and inverter current references in the d- and q-axes by regulating the DC side capacitor voltage. An LMI approach for stability analysis of the proposed controller, reliability calculations and switching losses are investigated. The accuracy of the developed qZSI inverter model and the effectiveness of proposed controls are verified by time-domain simulations of a study system in the MATLAB/SIMULINK environment and hardware-in-the-loop (HIL) simulation. • We have modeled a qZSI based on the generalized state-space averaged method. • We have offered a synthetic perspective over control design. • We performed a nonlinear controller stability analysis using the LMI approach. • We proposed a hardware-in-the-loop real-time testbed for qZSI control. [ABSTRACT FROM AUTHOR]

Piezoelectric quasicrystals have a broad spectrum of promising applications and research value due to their unique atomic arrangement and multi-field coupling effects. This paper presents a mechanical analysis of the static bending and free vibration problems of two-dimensional multilayered decagonal piezoelectric quasicrystal laminated beams. Mechanical models of the beams are established, and the differential quadrature method in conjunction with the state-space method is utilized to obtain the solutions for the beams with mixed boundary conditions. We first investigated the static response and free vibration problem of quasicrystal laminated beams. By degrading them into crystalline materials, we verified the accuracy of the method by comparing the results with the existing ones. Then, the effects of different boundary conditions and slenderness ratios on the stresses, displacements, electric potentials, and electric displacements of quasicrystal laminated beams under normal mechanical loading and free vibration are investigated. Subsequently, the effects of different laying modes on the mechanical properties of the quasicrystal laminated beams when the material principal axis of the beam is changed are also analyzed. • Analysis of the static bending and free vibration problems of multilayered 2D decagonal PQC beams is established. • Mixed boundary conditions are considered by using differential quadrature method and state-space method. • Orthogonal and diagonal laying modes are investigated by rotating the material stiffness matrixes. • Effects of different boundary conditions and slenderness ratios on the static bending and free vibration are analyzed. [ABSTRACT FROM AUTHOR]