Steady-state Gain Research Articles

Oculometric biomarkers of visuomotor deficits in clinically asymptomatic patients with systemic lupus erythematosus undergoing long-term hydroxychloroquine treatment.

This study examines a set of oculomotor measurements, or "oculometric" biomarkers, as potential early indicators of visual and visuomotor deficits due to retinal toxicity in asymptomatic Systemic Lupus Erythematosus (SLE) patients on long-term hydroxychloroquine (HCQ) treatment. The aim is to identify subclinical functional impairments that are otherwise undetectable by standard clinical tests and to link them to structural retinal changes. We measured oculomotor responses in a cohort of SLE patients on chronic HCQ therapy using a previously established behavioral task and analysis technique. We also examined the relationship between oculometrics, OCT measures of retinal thickness, and standard clinical perimetry measures of visual function in our patient group using Bivariate Pearson Correlation and a Linear Mixed-Effects Model (LMM). Significant visual and visuomotor deficits were found in 12 asymptomatic SLE patients on long-term HCQ therapy compared to a cohort of 17 age-matched healthy controls. Notably, six oculometrics were significantly different. The median initial pursuit acceleration was 22%, steady-state pursuit gain 16%, proportion smooth 7%, and target speed responsiveness 31% lower, while catch-up saccade amplitude was 46% and fixation error 46% larger. Excluding the two patients with diagnosed mild toxicity, four oculometrics, all but fixation error and proportion smooth, remained significantly impaired compared to controls. Across our population of 12 patients (24 retinae), we found that pursuit latency, initial acceleration, steady-state gain, and fixation error were linearly related to retinal thickness even when age was accounted for, while standard measures of clinical function (Mean Deviation and Pattern Standard Deviation) were not. Our data show that specific oculometrics are sensitive early biomarkers of functional deficits in SLE patients on HCQ that could be harnessed to assist in the early detection of HCQ-induced retinal toxicity and other visual pathologies, potentially providing early diagnostic value beyond standard visual field and OCT evaluations.

Identifying time-varying dynamics of heart rate and oxygen uptake from single ramp incremental running tests

Objective. The fact that ramp incremental exercise yields quasi-linear responses for pulmonary oxygen uptake ( V˙O2 ) and heart rate (HR) seems contradictory to the well-known non-linear behavior of underlying physiological processes. Prior research highlights this issue and demonstrates how a balancing of system gain and response time parameters causes linear V˙O2 responses during ramp tests. This study builds upon this knowledge and extracts the time-varying dynamics directly from HR and V˙O2 data of single ramp incremental running tests. Approach. A large-scale open access dataset of 735 ramp incremental running tests is analyzed. The dynamics are obtained by means of 1st order autoregressive and exogenous models with time-variant parameters. This allows for the estimates of time constant (τ) and steady state gain (SSG) to vary with work rate. Main results. As the work rate increases, τ-values increase on average from 38 to 132 s for HR, and from 27 to 35 s for V˙O2 . Both increases are statistically significant (p < 0.01). Further, SSG-values decrease on average from 14 to 9 bpm (km·h−1)−1 for HR, and from 218 to 144 ml·min−1 for V˙O2 (p < 0.01 for decrease parameters of HR and V˙O2 ). The results of this modeling approach are line with literature reporting on cardiorespiratory dynamics obtained using standard procedures. Significance. We show that time-variant modeling is able to determine the time-varying dynamics HR and V˙O2 responses to ramp incremental running directly from individual tests. The proposed method allows for gaining insights into the cardiorespiratory response characteristics when no repeated measurements are available.

Direct reconstruction of tissue temperature fields during thermal therapy by dynamic response matching and steady-state correction

In the field of oncology thermotherapy technology, online and real-time reconstruction of biological tissue temperature field is a fundamental work with important practical needs. In this paper, we proposed a new method for direct reconstruction of biological tissue temperature field by dynamic response feature matching and steady-state correction. Based on the bioheat transfer equation, the step response model of tissue temperature field during thermal therapy is established, and the dynamic response eigenvectors of biological tissue temperature and the steady-state gain of temperature response are determined offline. According to the optimal matching principle between the temperature dynamic response of the observation point and the internal space point of tissue, the optimal matching matrix of the dynamic response features between the observation point temperature and the internal temperature field of tissue is determined with the use of the dynamic response eigenvectors. Further, through the weighted synthesis of observation point temperature response by using the optimal matching matrix of dynamic response features, the tissue transient temperature field pre-estimation model is established. Finally, by correcting the tissue temperature field pre-estimation model with steady-state gain correction factor, the direct reconstruction scheme of temperature field based on dynamic response feature matching and steady-state gain correction is established. In this work, the non-destructive reconstruction of the internal temperature field of biological tissues during laser-induced thermal therapy is achieved based on the above direct reconstruction scheme. Numerical simulation experiments are performed to investigated the effects of pre-estimation time domain, the number of measurement points, laser irradiation form and measurement error on the temperature field reconstruction results.

Research on Power Balance Control Method of Electronic Transformer

Aiming at the problem of poor output stability and power gain of the power electronic transformer of the microgrid energy storage system, a power balance control method for the power electronic transformer of the microgrid energy storage system based on parallel modular multi-level commutation control is proposed. The reactive power outer loop parameter fusion method is used to sample the power dynamic load parameters and information fusion processing for the transformers in the system. According to the static limit active power adjustment of the fuzzy parameter factors on the output voltage side of the DC port, the steady-state gain of the power electronic transformer of the microgrid energy storage system is calculated. The static current limiting adjustment method is used to construct the feedback compensation model of the power electronic transformer of the microgrid energy storage system. In the active power control loop, the optimal design of the power electronic transformer power balance control of the microgrid energy storage system is realized through transient stable power adjustment. The test results show that using this method to perform power balance control on the power electronic transformer of the microgrid energy storage system has high output stability and good active power balance, which improves the robustness and adaptability of the transformer balance control.

Optimal PID control with anti-windup in neutralization process

PID control, which is a type of automation, was used to ensure that neutralization takes place in a controlled manner. To determine the PID parameters of the system with the Cohen-Coon tuning method, two different dynamic experiments were carried out for pH and temperature in the first stage, and the transfer function and model parameters were found. In the experiment carried out for the pH variable; time constant (τ) is 59 s, dead time (Ɵ) is 261 s and steady state gain (K) is read from the graph as 14,72, while PID parameters are calculated as KC =0.0375, τI =315.759 s and τD =52.601 s. Likewise, while τ=1402 s., Ɵ= 88 s, and K=-6 were read for the temperature variable, the PID parameters were calculated as KC =6.196, τI =47.23 s, and τD =-19.20 s. The determined controller parameters were used as initial parameters and simulated using the S-function block via MATLAB (2007b). The pH set range was coded as 6.5-8.5 and Tset for temperature was coded as 22 °C. As a result of the oscillation observed due to the nature of the PID control parameters that are intended to be controlled, the safe operation of the process and the desired set values are ensured. When the obtained PID controller parameters were applied to the neutralization reaction, the PID control successfully controlled the reactor temperature and pH and eliminated possible hazards in operation. Anti-windup provides better control rather than traditional PID control method.

Resonant behaviors of two coupled fluctuating-frequency oscillators with tempered Mittag-Leffler memory kernel

The dynamics of coupled oscillators and their collective behaviors are of great significance in various fields. This paper explores the resonant behaviors in the system composed of two coupled fluctuating-frequency oscillators with tempered Mittag-Leffler (M-L) memory kernel. The study begins by demonstrating that the two particles synchronize in the long-time limit, indicating that the mean-field behavior aligns with that of each individual particle. Accordingly, we derive an exact analytical expression for the steady-state output amplitude gain (OAG) of the mean field using the stochastic average method. Furthermore, the research investigates the diverse phenomena of generalized stochastic resonance (GSR) and examines how various system parameters influence GSR. To validate the theoretical analysis, numerical simulations are conducted to support and verify our findings.

Kernel-Based Impulse Response Identification With Side-Information on Steady-State Gain

In this paper, we consider the problem of system identification when side-information is available on the steady-state gain (SSG) of the system. We formulate a general nonparametric identification method as an infinite-dimensional constrained convex program over the reproducing kernel Hilbert space (RKHS) of stable impulse responses. The objective function of this optimization problem is the empirical loss regularized with the norm of RKHS, and the constraint is considered for enforcing the integration of the SSG side-information. The proposed formulation addresses both the discrete-time and continuous-time cases. We show that this program has a unique solution obtained by solving an equivalent finite-dimensional convex optimization. This solution has a closed-form when the empirical loss and regularization functions are quadratic and exact side-information is considered. We perform extensive numerical comparisons to verify the efficiency of the proposed identification methodology.

Two-stage stochastic unit commitment for renewable energy integrated power systems considering dynamic capacity-increase technologies of transmission lines

Dynamic capacity-increase technologies (DCITs), as a solution to reduce the stress of transmission networks, avoid long-term constructions and update the maximum transmitted capacities based on real-time environment factors and the maximum allowable operation temperatures (MAOTs), which provide flexibility in optimizing the operation modes for power systems. This paper proposes a detailed two-stage stochastic unit commitment model considering the physical processes of heat transfer as well as the uncertainties of renewable energy. The dynamic thermal rating (DTR) is calculated based on the heat balance of transmission lines in the steady state, including radiated and convective heat loss and gain from the solar heat and power flows. To simulate the uncertainties from renewable energy outputs, this paper applies the Latin hypercube sampling method to generate output scenarios in stochastic optimization. Finally, the proposed model is verified on a modified IEEE-24-bus test system. The effectiveness of DCITs is illustrated through the case study.

Improving INS/GPS Loosely fusion, during Spoofing, using steady-state Kalman matrix gain

Improving INS/GPS Loosely fusion, during Spoofing, using steady-state Kalman matrix gain

Designing multivariable PI controller with multi-response optimization for a pilot plant binary distillation column: a robust design approach

Purpose The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates multiple responses while considering the process's control and noise parameters. In addition, this paper intended to develop a multidisciplinary approach by combining computational science, control engineering and statistical methodologies to ensure a resilient process with the best use of available resources. Design/methodology/approach Taguchi's robust design methodology and multi-response optimisation approaches are adopted to meet the research aims. Two-Input-Two-Output transfer function model of the distillation column system is investigated. In designing the control system, the Steady State Gain Matrix and process factors such as time constant (t) and time delay (?) are also used. The unique methodology is implemented and validated using the pilot plant's distillation column. To determine the robustness of the proposed control system, a simulation study, statistical analysis and real-time experimentation are conducted. In addition, the outcomes are compared to different control algorithms. Findings Research indicates that integral control parameters (Ki) affect outputs substantially more than proportional control parameters (Kp). The results of this paper show that control and noise parameters must be considered to make the control system robust. In addition, Taguchi's approach, in conjunction with multi-response optimisation, ensures robust controller design with optimal use of resources. Eventually, this research shows that the best outcomes for all the performance indices are achieved when Kp11 = 1.6859, Kp12 = −2.061, Kp21 = 3.1846, Kp22 = −1.2176, Ki11 = 1.0628, Ki12 = −1.2989, Ki21 = 2.454 and Ki22 = −0.7676. Originality/value This paper provides a step-by-step strategy for designing and validating a multi-response control system that accommodates controllable and uncontrollable parameters (noise parameters). The methodology can be used in any industrial Multi-Input-Multi-Output system to ensure process robustness. In addition, this paper proposes a multidisciplinary approach to industrial controller design that academics and industry can refine and improve.

Modeling microgrid power flow dynamics with covariance based realization

Reducing transient effects of power quality in the distribution network of a microgrid requires a better understanding of the dynamics of power flow and distributed energy resources (DERs) present within the microgrid. Instead of labor intensive and detailed physics-based distribution network and DER modeling, this paper shows how power flow dynamics can also be modeled in a dynamic equivalent with a data-driven approach that exploits the time dependent covariance of power flow at different locations within a microgrid. The proposed approach results in multi-input multi-output (MIMO) linear time invariant (LTI) discrete-time filter concatenated by a static nonlinearity to model both dynamics and power losses. Estimation of the MIMO LTI dynamics is done by the Covariance Based Realization Algorithm (CoBRA) that can handle noisy perturbed measurements and provide a low order MIMO LTI filter with a normalized steady-state gain. The additional static nonlinearity is found by the estimation of the admittance matrix of the distribution network within the microgrid. The main contribution of this paper is proposing a physics-informed data-driven approach to modeling microgrid power flow dynamics, where the CoBRA method is implemented to learn parameters from data while observing a priori physical constraints. The approach is illustrated on experimental data from a microgrid with multiple DERs and it is shown that an excellent agreement in power flow dynamics is obtained over a large operating range, which demonstrates its advantage over the N4SID method.

Approximation of Fractional-Order Systems Using Balanced Truncation with Assured Steady-State Gain

Approximation of Fractional-Order Systems Using Balanced Truncation with Assured Steady-State Gain

Research on Covert Directional Spoofing Method for INS/GNSS Loosely Integrated Navigation

To meet the task requirements of using GNSS spoofing signals to achieve directional spoofing of unmanned target platforms, this paper proposes an INS/GNSS integrated navigation covert directional spoofing method, which can be used in key place security defense and public security protection scenarios. Taking the steady-state gain matrix as a breakthrough, this paper firstly studies the influence mechanism of GNSS spoofing signals on the position output of INS/GNSS integrated navigation, and then deduces the feasibility of directional spoofing by using exponential spoofing signal with two adjustable parameters in INS/GNSS integrated navigation mode. Then, the influence of spoofing signals on the attitude and speed of the integrated navigation output is analyzed, and the relevant constraints of velocity and attitude errors are introduced to determine the exponential GNSS spoofing signals model that meets the requirements of covertness, and finally the task of covert directional spoofing of the target unmanned platform is realized. Simulation and experiments verify the effectiveness and correctness of INS/GNSS covert directional spoofing method.

MIMU Optimal Redundant Structure and Signal Fusion Algorithm Based on a Non-Orthogonal MEMS Inertial Sensor Array.

A micro-inertial measurement unit (MIMU) is usually used to sense the angular rate and acceleration of the flight carrier. In this study, multiple MEMS gyroscopes were used to form a spatial non-orthogonal array to construct a redundant MIMU system, and an optimal Kalman filter (KF) algorithm was established by a steady-state KF gain to combine array signals to improve the MIMU's accuracy. The noise correlation was used to optimize the geometric layout of the non-orthogonal array and reveal the mechanisms of influence of correlation and geometric layout on MIMU's performance improvement. Additionally, two different conical configuration structures of a non-orthogonal array for 4,5,6,8-gyro were designed and analyzed. Finally, a redundant 4-MIMU system was designed to verify the proposed structure and KF algorithm. The results demonstrate that the input signal rate can be accurately estimated and that the gyro's error can also be effectively reduced through fusion of non-orthogonal array. The results for the 4-MIMU system illustrate that the gyro's ARW and RRW noise can be decreased by factors of about 3.5 and 2.5, respectively. In particular, the estimated errors (1σ) on the axes of Xb, Yb and Zb were 4.9, 4.6 and 2.9 times lower than that of the single gyroscope.

Order Reduction of Large-Scale Linear Dynamic Systems Using Balanced Truncation with Modified Cauer Continued Fraction

This article discusses a new technique to reduce the order of high-dimensional, continuous, linear time-invariant dynamic systems. The proposed approach utilizes the merits of the balanced truncation method, and modified Cauer continued fraction expansion. The reduced model denominator was retrieved via the concept of Lyapunov balancing, and state truncation and numerator coefficients were obtained by using a modified Cauer continued fraction. The proposed technique circumvents the steady-state gain discrepancy and steady-state error mismatch of the conventional balanced truncation method, and the instability issue of continued fraction expansion. The proposed method assures steady-state gain approximation, few moments, Markov parameters, stability, and other essential control features of a high-order system. The efficacy and applicability of the proposed technique are demonstrated using some typical test systems adopted from the literature. This method gives improved performance indices like minimum H∞ norm, H2 norm, a minimum ISE (integral squared error), IAE (integral absolute error), and retains approximately the full IRE (impulse response energy) of the existing system in the reduced model. The simulation findings of the proposed technique are compared with other current model reduction methods in recent literature using MATLAB.

Differential saccade-pursuit coordination under sleep loss and low-dose alcohol.

Ocular tracking of a moving object requires tight coordination between smooth pursuit and saccadic eye movements. Normally, pursuit drives gaze velocity to closely match target velocity, with residual position offsets corrected by catch-up saccades. However, how/if common stressors affect this coordination is largely unknown. This study seeks to elucidate the effects of acute and chronic sleep loss, and low-dose alcohol, on saccade-pursuit coordination, as well as that of caffeine. We used an ocular tracking paradigm to assess three metrics of tracking (pursuit gain, saccade rate, saccade amplitude) and to compute "ground lost" (from reductions in steady-state pursuit gain) and "ground recouped" (from increases in steady-state saccade rate and/or amplitude). We emphasize that these are measures of relative changes in positional offsets, and not absolute offset from the fovea. Under low-dose alcohol and acute sleep loss, ground lost was similarly large. However, under the former, it was nearly completely recouped by saccades, whereas under the latter, compensation was at best partial. Under chronic sleep restriction and acute sleep loss with a caffeine countermeasure, the pursuit deficit was dramatically smaller, yet saccadic behavior remained altered from baseline. In particular, saccadic rate remained significantly elevated, despite the fact that ground lost was minimal. This constellation of findings demonstrates differential impacts on saccade-pursuit coordination with low-dose alcohol impacting only pursuit, likely through extrastriate cortical pathways, while acute sleep loss not only disrupts pursuit but also undermines saccadic compensation, likely through midbrain/brainstem pathways. Furthermore, while chronic sleep loss and caffeine-mitigated acute sleep loss show little residual pursuit deficit, consistent with uncompromised cortical visual processing, they nonetheless show an elevated saccade rate, suggesting residual midbrain and/or brainstem impacts.

Design of New Nonisolated High Gain Converter for Higher Power Density

A high gain nonisolated DC-DC converter using a single power semiconductor switch is proposed in this article. The operation of the proposed converter is explained under continuous conduction mode (CCM), discontinuous conduction mode (DCM), and boundary conduction mode (BCM). The mathematical expressions for steady-state voltage gain, voltage stress, and current stress of diodes and switch are provided. Also, the design of inductors and capacitors in the CCM mode is explained with appropriate mathematical equations. The proposed topology is tested with a 200 W prototype at 50 kHz and a 60% duty cycle. The dynamic behavior of the proposed converter is examined by changing the duty cycle value and also load values. The proposed converter is verified with experimental results to prove the effectiveness of its operation. The proposed converter provides higher steady-state voltage gain as compared with recently developed topologies. The efficiency and power density of the proposed converter is 90% and 1.16 kW/L, respectively.

Wide-bandwidth cutting force monitoring via motor current and accelerometer signals

Cutting forces are valuable for monitoring machining processes, as they allow the evaluation of tool wear, process stability, and workpiece quality. This paper presents a new non-intrusive method for monitoring cutting forces using a Kalman filter (KF) and Rauch-Tung-Striebel (RTS) smoothing. The method incorporates structural acceleration measurements as well as feed drive motor currents, thereby achieving wide estimation bandwidth and steady-state (DC) gain preservation. The KF + RTS smoothing are parameterized using spindle speed-sweeping cutting tests, which significantly reduces the experimental effort for model identification. The approach was validated in different machining conditions, which demonstrated good agreement with dynamometer measurements.

Dynamics and control of large-scale fluidized bed plants for renewable heat and power generation

As the share of variable renewable electricity increases, thermal power plants will have to adapt their operational protocols in order to remain economically competitive while also providing grid-balancing services required to deal with the inherent fluctuations of variable renewable electricity. This work presents a dynamic model of fluidized bed combustion plants for combined heat and power production. The novelty of the work lays in that (i) it provides an analysis of the transient performance of biomass-based fluidized bed combustion plants for combined heat and power production, (ii) the dynamic model includes a description of both the gas and water-steam sides and (iii) the model is validated against operational data acquired from a commercial-scale plant. The validated model is here applied to analyze the inherent dynamics of the investigated plant and to evaluate the performance of the plant when operated under different control and operational strategies, using a relative gain analysis and a variable ramping rate test.The results of the simulations reveal that the inherent dynamics of the process have stabilization times in the range of 5–25 min for all the step changes investigated, with variables connected to district heating production being the slowest. In contrast, variables connected to the live steam are the fastest, with stabilization times of magnitude similar to those of the in-furnace variables (i.e., around 10 min). Thus, it is concluded that the proper description of the dynamics in fluidized bed combustion plants for combined heat and power production requires modeling of both the gas and water sides (which is rare in previous literature). Regarding the assessment of control strategies, the boiler-following and hybrid control (combined fixed live steam and sliding pressure) strategies are found to be able to provide load changes as fast as −5%-unit/s, albeit while causing operational issues such as large pressure overshoots. The relative gain analysis outcomes show that these control structures do not have a steady-state gain on the power produced, and therefore it is the dynamic effect of the steam throttling that triggers the rapid power response. This study also includes the assessment of a turbine bypass strategy, the results of which show that it enables fast load-changing capabilities at constant combustion load, as well as decoupling power and heat production at the expense of thermodynamic losses.

A Raman quantum free-electron laser model

Operation of a Quantum Free Electron Laser (QFEL) could provide a compact and fully coherent source of X- and γ-rays. Imperative to experimental realization is allowing for decoherence effects of either spontaneous emission or space-charge to take place, having opposing constraints. Here, for the first time, we present a one-dimensional QFEL Wigner model that includes longitudinal space-charge effects by quantizing the periodic potential derived from the Fourier components of the longitudinal electron beam density. The model is used to investigate steady-state QFEL gain and momentum state dynamics for a variety of space-charge regimes. We find increased saturation lengths and lower saturation intensity as a result of attenuated transitions in the two-level quantum system. In addition, we characterize a space-charge regime where specific transitions outside the QFEL bandwidth are targeted, such that the conventional description breaks down. These findings serve as a consistent theoretical extension of existing QFEL models.