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Energy level interaction and electron concentration are crucial aspects that affect the response performance of quantum cascade detectors (QCDs). In this work, two different-structured array QCDs are prepared, and the detectivity reaches 109 cm·Hz1/2/W at room temperature. The overlap integral (OI) and oscillator strength (OS) between different energy levels under a series of applied biases are fitted and reveal the influence of energy level interaction on the response performance. The redistribution of electrons in the cascade structure at room temperatures is established. The coupled doped-well structure shows a higher electron concentration at room temperature, which represents a high absorption efficiency in the active region. Even better responsivity and detectivity are exhibited in the coupled doped-well QCD. These results offer a novel strategy to understand the mechanisms that affect response performance and expand the application range of QCDs for long-wave infrared (LWIR) detection. [ABSTRACT FROM AUTHOR]

In this article, a gray-box parsimonious subspace identification method using sampled dynamical and steady-state data is proposed for block-oriented Hammerstein-type systems. Compared with the conventional subspace identification methods based on over-parameterized models, the proposed method assumes parsimonious models, where the number of parameters is as minimal as possible to ensure the model accuracy, especially for highly nonlinear models. Generally, the available dynamical data do not contain adequate information on the low-frequency characteristics of the system. To improve the accuracy of model identification, the steady-state information of the system is taken into account, and a multiregularization method is developed, where the whole model parameters are estimated in a hierarchical, iterative manner from two sets of data: the dynamical input–output data and the steady-state input–output data. The use of parsimonious models and hierarchical estimation can significantly reduce the size of the associated parameter estimation error covariance matrix, thus improving the model accuracy and decreasing the variance of the estimated parameters, compared with the over-parametrization methods only using dynamical data. The effectiveness and merits are demonstrated by a simulation example and a real-world example. [ABSTRACT FROM AUTHOR]

This paper shows how existing methods of time series analysis and modeling can be exploited in novel ways to monitor and forecast the COVID-19 epidemic. In the past, epidemics have been monitored by various statistical and model metrics, such as evaluation of the effective reproduction number, R (t). However, R (t) can be difficult and time consuming to compute. This paper suggests two relatively simple data-based metrics that could be used in conjunction with R (t) estimation and provide rapid indicators of how the epidemic's dynamic behavior is progressing. The new metrics are the epidemic rate of change (RC) and a related state-dependent response rate parameter (RP), recursive estimates of which are obtained from dynamic harmonic and dynamic linear regression (DHR and DLR) algorithms. Their effectiveness is illustrated by the analysis of COVID-19 data in the UK and Italy. The paper also shows how similar methodology, combined with the refined instrumental variable method for estimating hybrid Box–Jenkins models of linear dynamic systems (RIVC), can be used to relate the daily death numbers in the Italian and UK epidemics and then provide 15-day-ahead forecasts of the UK daily death numbers. The same approach can be used to model and forecast the UK epidemic based on the daily number of COVID-19 patients in UK hospitals. Finally, the paper speculates on how the state-dependent parameter (SDP) modeling procedures may provide data-based insight into a nonlinear differential equation model for epidemics such as COVID-19. [ABSTRACT FROM AUTHOR]

This article develops a new method of data-driven modeling for a class of multiple-transmitter single-receiver wireless power transfer (WPT) systems. A continuous-time multiple-input single-output (MISO) model with pure time delays is used to characterize the input–output behavior of the system, where the transfer functions associated with each input channel are not constrained to have the same denominator. Moreover, the time delays are allowed to be a fraction of the sampling interval in order to account for the delay effects that stem from circuit components and wireless communication, which are, by nature, often a fraction of the sampling interval. An optimal refined instrumental variable method is proposed to estimate the parameters and time delays of the MISO model based on sampled input–output data. In contrast to the conventional circuit-theory-based modeling methods that rely on circuit parameters and result in models which are often complex, the proposed data-based method yields parsimonious models, whose parameters are directly estimated from input–output data. Due to the easy availability of sampled data in control engineering applications, the proposed method is clearly more user-friendly, having a broad prospect for efficient operation of WPT systems, such as prediction, optimization, and control. Numerical and experimental results are presented to validate the effectiveness and merit of the proposed method. [ABSTRACT FROM AUTHOR]

This article considers the data-driven modeling of a class of phase-controlled wireless power transfer (WPT) systems, where the load may vary slowly with respect to time. The dominant mode analysis suggests that a model of the Hammerstein type, which consists of a static nonlinearity function, followed by a linear time-varying model with a pure time delay, is the best structure to describe the input–output relationship of the system. On this basis, we derive a small-signal model that is linear in the variables in order to aid control design and allow the associated model parameters to be estimated from sampled input–output data using the standard refined instrumental variable (RIV) method. In the presence of a time-varying load, however, the plant model parameters may not be correctly estimated if the load response is not removed. In order to address this problem, a new recursive RIV method is proposed, in which an effective technique is introduced to track the load response, so allowing the parameters and time delay of the time-varying model to be accurately estimated. The effectiveness of the proposed method is verified by applying it to both a simulation model and a laboratory system. [ABSTRACT FROM AUTHOR]

This paper is concerned with control-oriented modeling of a class of wireless power transfer systems with a phase-shift-controlled inverter. Two methods are investigated: the analytical modeling method, which is based on physical laws, and the refined instrumental variable method, which is based on sampled data. It shows that the former provides physical insights into the system, and that the resulting models will be accurate if the true circuit component parameters are a priori known. Otherwise, the model accuracy may decline. By contrast, the latter provides a cheap solution for accurate modeling, based only on a set of sampled input–output data, and the resulting model can capture very well the dynamic behavior of the system within a defined operating range. In real applications, it is always hard to know the actual circuit component parameters, since they may drift from the nominal values, due to some reasons such as the aging of circuit components and the variation of coil placement, making the analytical modeling method not very accurate. Therefore, to preserve the virtues of both methods, it is suggested to use them as a combination, that is, using the analytical modeling method to determine the model structure while using the data-driven modeling method to estimate the model parameters. All results derived in this paper are verified by means of both numerical simulation and experimental validation. [ABSTRACT FROM AUTHOR]

• A difference operator is proposed to reject the disturbance effect and a new instrumental variable is proposed to eliminate the noise effect for consistent estimation. • The new instrumental variable enhances the estimated model efficiency/accuracy, as supported by consistent analysis. • The auxiliary static-gain information is used to enhance the accuracy of the estimated quantities. • Compared with SIMs using no priori-knowledge, the proposed method could reduce the estimation error variances of the system matrices B and D, as supported by variance analysis. • A prior knowledge-based subspace identification method is proposed for batch processes subject to repeatable disturbances. In this paper, a prior-knowledge-based subspace identification method (SIM) is proposed for batch processes subject to repeatable disturbances. The proposed method is a two-step procedure for state-space model identification: in the first step, the extended observability and triangular Toeplitz matrices are estimated simultaneously from a parity space of the experimental data and, based on which, the corresponding system matrices are retrieved in the second step. More specifically, A and C are retrieved from the estimated extended observability matrix, while B and D are retrieved from the estimated triangular Toeplitz matrix. The proposed method shows several superiorities in the following aspects. Firstly, it is able to provide unbiased parameter estimation in the presence of repeatable disturbances, thanks to the proposed difference operator which eliminates the disturbance effect. Secondly, it shows better robustness to measurement noise compared with the existing SIMs using parity space, due to the inherent instrumental variable mechanism and the new technique to build the instrument, which greatly enhance the estimated model efficiency/accuracy. Lastly, by taking the auxiliary static-gain information into account in the identification procedure, the variance properties of the parameters can be improved, especially for the system matrices B and D. All the above-mentioned developments are analyzed with strict mathematical proofs, along with two illustrative examples to confirm the effectiveness and merits. [ABSTRACT FROM AUTHOR]

In this study, a fixed point iteration‐based subspace identification method is proposed for Hammerstein state‐space systems. The original system is decomposed into two subsystems with fewer parameters based on the hierarchical identification principle. Each subsystem is related directly to either the linear dynamics or the static non‐linearity. A two‐stage least‐squares‐based iterative method is then implemented to separately estimate the coefficients of the non‐linear subsystem and the extended Markov parameters of the linear subsystem. The linear subsystem parameters are extracted from the identified extended Markov parameters using a singular value decomposition based method. Convergence analysis of the proposed method is established using fixed point theory, which shows that the proposed method gives consistent estimates under arbitrary non‐zero initial conditions. Simulation results are included to show the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract This paper is concerned with identification of continuous-time output-error models with time-delay from relay feedback tests. Conventional methods for solving this problem consist in deriving analytical limit cycle expressions and fitting them to measured shape factors. However, they may fail to handle different limit cycles uniformly, due to the structural differences in the analytical expressions. To overcome this problem, we consider a more general, data-based, parametric identification framework using sampled limit cycle data. A frequency domain method that minimizes the sum of squared output-errors is developed. The proposed method can be of high accuracy, thanks to the periodic input–output signals provided by sustained relay feedback oscillations, which can help to reduce leakage and aliasing errors. Besides, a distinctive merit of the proposed method is that identification of stable and unstable plants can be equally simple. The effectiveness and superiority of the proposed method are demonstrated by means of both theoretical analyses and simulation examples. [ABSTRACT FROM AUTHOR]

This paper discusses several issues related to the identification of time-delayed continuous-time systems using the refined instrumental variable method. The proposed estimation procedure is iterative where, at each iteration, the rational system parameters and time-delay are estimated separately. The main contribution of this paper covers three aspects. Firstly, conditions for persistent excitation are established, which should be satisfied to guarantee the identifiability of the system parameters and time-delay. Secondly, existence of multiple minima in the loss function is investigated. Due to the nonlinear nature of the loss function to be optimized with respect to the time-delay, initialization is a particularly important issue for correct estimation of the time-delay. Lastly, to reliably initialize the identification algorithm, based on the previous analysis, some guidelines are proposed to facilitate the choice of reliable initial parameters. The main results derived in this paper are verified by means of both theoretical analyses and numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, an output error method is proposed for the identification of continuous-time systems with time delay from sampled data. The challenge of time delay system identification lies in the presence of nonlinear time delays in models, then starting value-based optimization methods may be trapped easily by local minima. In order to improve the convergence performance to the choice of initial parameters, several approaches to smooth the loss function are presented. It is shown that the loss function may possess many local minima when data are regularly sampled with the inter-sample behavior of zero-order hold. Interestingly, irregular sampling can be an efficient approach to overcome these local minima. To achieve superior convergence performance, an over-parametrization approach incorporating a low-pass filtering technique is proposed to enlarge the convergence region. Theoretical and simulated results are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper we present a novel algorithm for identifying continuous-time autoregressive moving-average models utilizing irregularly sampled data. The proposed algorithm is based on the expectation–maximization algorithm and obtains maximum-likelihood estimates. The proposed algorithm shows a fast convergence rate, good robustness to initial values, and desirable estimation accuracy. Comparisons are made with other algorithms in the literature via numerical examples. [ABSTRACT FROM AUTHOR]

This study investigates the estimation of continuous‐time Box–Jenkins model parameters from irregularly sampled data. The Box–Jenkins structure has been successful in describing systems subject to coloured noise, since it contains two sub‐models that feature the characteristics of both plant and noise systems. Based on plant‐noise model decomposition, a two‐step iterative procedure is proposed to solve the estimation problem, which consists of an instrumental variable method for the plant model and a prediction error method for the noise model. The proposed method is of low complexity and shows good estimation robustness and accuracy. Implementation issues are discussed to improve the computational efficiency. Numerical examples are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to identify continuous-time systems with arbitrary time-delay from irregularly sampled input–output data. It is based on the separable nonlinear least-squares method which combines in a bootstrap manner the iterative optimal instrumental variable method for transfer function model estimation with an adaptive gradient-based technique that searches for the optimal time-delay. Since the objective function may have several local minima with respect to the unknown parameters (especially the time-delay), the initialization requires special attention. Here, a low-pass filtering strategy is used to widen the convergence region around the global minimum. Simulation results are included to show the performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with estimating the parameters of single-input, continuous-time systems from sampled input–output data, where any input time delays may be a fraction of the sampling interval. The proposed method estimates the parameters by minimizing a cost function defined by the sum of squared errors between the predicted and measured outputs using the optimal Refined Instrumental Variable method for Continuous-time models (RIVC). Potential difficulties may be encountered when standard gradient-based optimization is used for solving this optimization problem directly. These arise because the cost function is normally multi-modal with respect to the fractional time delay and they can occur for several reasons, such as aliasing errors, the nature of the system dynamics, and the selection of the excitation signal. In order to avoid the problems caused by these local minima, a two-stage procedure is proposed combining RIVC estimation of the model parameters with a grid search for the fractional time delay. Although this algorithm is not quite as computationally efficient as standard gradient-based optimization, it is simple, robust, and more widely applicable, as illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

Due to various advantages, wireless charging as well as wired charging for electrical vehicles (EV) will co-exist for some time to come. A hybrid system that provides either wired or wireless charging mode is proposed in the paper. The hybrid system shares the workplaces and the power devices to save costs significantly. The cost of DC power module accounts for a large proportion of the total cost of the wired or wireless charging system and is directly related to its rated power. To reduce the rated power requirement of the DC module of the hybrid charging system, an optimized parameter design method regarding the coupling inductance of the wireless charging is presented for the hybrid system. Under the optimized coupling inductance, the minimum rated DC power requirement is achieved while meeting the battery charging profile of both the wired and wireless charging application. An EV hybrid charging system prototype is designed, built, and tested. Experiments showed that the proposed hybrid charging system has good adaptability for either charging method. Moreover, the proposed parameter design procedure can minimize the rated power requirement of the DC power module. [ABSTRACT FROM AUTHOR]

In severe low-visibility environments full of smoke, because of the performance degeneration of the near-infrared (NIR) collimation system of quantum drones communication networks, the improved dual-threshold method based on trend line analysis for long-wave infrared (LWIR) quantum cascade lasers (QCLs) is proposed, to achieve target acquisition. The simulation results show that smoke-scattering noise is a steeply varying medium–high-frequency modulation. At particle sizes less than 4 μ m, the traditional dual-threshold method can effectively distinguish the target information from the smoke noise, which is the advantage of the LWIR laser compared to the NIR laser. For detecting lasers with high signal-to-noise ratios (SNRs), the method can achieve good target acquisition, by setting reasonable conventional thresholds, such as 0.7 times the peak intensity and 0.8 times the peak rising velocity. At low SNRs and steep intensity variation, the method can also achieve good target acquisition, by adaptively resetting new thresholds after filtering the detecting laser, such as 0.6 times the peak intensity and 0.6 times the peak rising velocity. The results of this paper will provide a reference for the performance improvement and refinement of the collimation system for wireless quantum communication networks in low visibility. [ABSTRACT FROM AUTHOR]

The hypoxia-reoxygenation process of obstructive sleep apnea (OSA) may cause oxidative stress injury of the kidney, but the molecular mechanisms are not clear. The present study aimed to investigate whether high mobility group box 1 protein (HMGB1) and its subsequent inflammatory pathway served a role in kidney injury. Adult Sprague Dawley rats were used to establish hypoxia models: Continuous hypoxia, intermittent hypoxia and intermittent hypoxia with hypercapnia. Rat kidney tissues and peripheral blood samples were obtained. Histopathological and ultrastructural changes were observed by light and electron microscopy. Immunohistochemical (IHC) staining was used to detect the distribution of HMGB1. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of HMGB1, receptor for advanced glycosylation end products (RAGE), toll-like receptor 4 (TLR4), nuclear factor kappa-light-chain-enhancer of active B cells (NF-κB) p65 subunit, tumor necrosis factor-α (TNF-α), interleukin (IL)-6, NAD-dependent protein deacetylase sirtuin-1 (SIRT1), peroxisome proliferator-activated receptor (PPAR) mRNA in renal tissues. An ELISA was used to detect the expression of soluble TLR2, TLR4, PPAR-γ, TNF-α, IL-6 in peripheral blood. Hematoxylin & eosin staining demonstrated that there was no serious injury to the kidneys due to hypoxia, with the exception of a certain degree of renal tubular epithelial cell vacuolation. By contrast, ultrastructural changes by electron microscopy were more significant in the hypoxia groups compared with the control, including foot process fusion in the glomerulus and degeneration of mitochondria in the proximal convoluted tubules. IHC also indicated increased expression of HMGB1 and nuclear translocation in the hypoxia groups. The results of the RT-qPCR demonstrated that hypoxia stimulation increased the expression of HMGB1, PPAR, RAGE and TNF-α mRNA, and decreased the expression of SIRT1 mRNA in kidney tissues (P<0.05). The results of the ELISA suggested that hypoxia stimulation increased the expression of soluble TLR4, TNF-α and IL-6 in the peripheral blood, and decreased the expression of soluble TLR2 and PPAR-γ. In summary, hypoxia stimulation may cause early renal injury at the subcellular level and increase the expression and translocation of HMGB1. Hypoxia also upregulated the mRNA expression of the HMGB1-RAGE-TNF-α pathway in kidney tissue and increased the expression of soluble TLR4, TNF-α and IL-6 in the peripheral blood. This suggested that the HMGB1-RAGE/TLR-TNF-α pathway may contribute to the molecular mechanisms of early renal injury induced by hypoxia. The pathway may contain potential markers for OSA-associated early renal injury and drug intervention targets in the future. [ABSTRACT FROM AUTHOR]

Accurate modeling and state of charge (SOC) estimation of lithium-ion battery against the model uncertainty and data uncertainty are difficult tasks nowadays. In this paper, a model and data uncertainties-robust method is proposed simultaneous estimation of the model parameters and the SOC using an enhanced adaptive unscented Kalman filter (AUKF). An extended state observer is established to integrate all unknown variables including parameters and SOC into a vector. An covariance matching technique with adaptive forgetting factor is proposed to obtain uncertain model and data statistics, in combination with a singular value decomposition based unscented transform to guarantee the positive definiteness of the error covariance matrix. Furthermore, establishing new protocols to handle missing input and missing output separately, the battery SOC and parameters can be estimated from missing measurements. Benefits from above procedures, the proposed method is more robust to model uncertainties and the data uncertainties compared to the conventional SOC estimation method. The robustness of the proposed method is verified at different operation temperatures and dynamic load profiles. The results shows that the proposed method possesses high accuracy and excellent robustness. • A model and data uncertainties-robust joint estimation method is proposed. • Estimate battery parameters and SOC simultaneously based on ESO and AUKF. • SVD-based unscented transform ensure a positive definite error covariance matrix. • A covariance matching technique is employed for uncertain model and data statistics. • The protocols to handle battery missing input and output separately are established. [ABSTRACT FROM AUTHOR]

High-power, incoherent THz array sources are widely desired in some applications due to their low energy, unique terahertz fingerprint, and image. In this work, a dual ridge terahertz quantum cascade laser lasing at 3.1 THz is presented, and the device's performance is analyzed in detail. The maximum output power can reach 512 mW when the two ridges work simultaneously in continuous-wave mode, with a threshold current density of 281 A/cm2 at 15 K. While the peak power is approximately 704 mW in pulse-wave mode at 15 K, the lasing still could be observed approximately 7 mW at 125 K. The far-field pattern of the dual ridge THz QCL is detected by a THz camera; two light spots typically show a single-lobe Gaussian distribution. The experimental results provide a reference for realizing high-power THz quantum cascade lasers, and they will provide some guidance for the structural design of multiple ridges or laser arrays. [ABSTRACT FROM AUTHOR]

High-order gratings eliminate the need for high-resolution lithography in the definition process. However, they are not widely adopted due to inability to provide sufficient coupling coefficients in long-wave mid-infrared quantum cascade lasers (QCLs). Fifth order tilted asymmetric gratings (TAS-gratings) distributed feedback (DFB) QCLs with stable single longitudinal mode are firstly reported in this paper. TAS-gratings exhibit longitudinal mode selection capability across a broad range of asymmetric distances around 1.18 μm and duty cycles around 0.5, which is attributed to their larger effective duty cycles and phase differences caused by an 18° tilt angle of the grating in the middle and two asymmetrical distributions of gratings on both sides. Further experimental results indicate that DFB QCLs with TAS-gratings exhibit stable excitation of single longitudinal modes within the duty cycle range of 0.48–0.54 due to the large coupling coefficients, the temperature drift coefficient of 0.29 nm/K of TAS-grating device is reduced by 64% compared with symmetric gratings (S-gratings) devices, the maximum side-mode suppression ratio (SMSR) of the devices exceeds 25 dB. TAS-gratings offer greater process tolerance, design flexibility and lower preparation difficulty. This study provides a solution to achieve sufficiently large coupling coefficients for high-order gratings applied to long-wave mid-infrared QCLs. • Fifth-order tilted asymmetric surface gratings are proposed. • It's firstly found effective duty cycle is proportional to the asymmetric distance. • The grating has stable coupling coefficient and large process tolerance. • Device exhibits stable single longitudinal mode. [ABSTRACT FROM AUTHOR]

Modeling of Hammerstein–Wiener nonlinear systems has received a lot of attention in the signal processing community. However, all existing model identification methods may fail to provide a consistent parameter estimate for Errors-In-Variables (EIV) Hammerstein–Wiener systems, where both input–output data are contaminated by measurement white noises. In this paper, a bias-correction Least-Squares (LS) algorithm for consistent identification of EIV Hammerstein–Wiener systems with polynomial nonlinearities using noisy measurements is proposed. Firstly, the analytic expression for the estimated bias of the LS algorithm using noisy measurements for EIV Hammerstein–Wiener systems with polynomial nonlinearities is derived, which is caused by the correlation between the input–output signals and measurement noises. Secondly, a consistent estimation method for the bias-correcting term, including a recursive step and a cross-validation step based on the available noisy measurements only, is then proposed to estimate the unknown terms of noises variances and noise-free measurements in the estimated bias. The effectiveness of the proposed algorithm is demonstrated through a simulated example and a robot arm system. [ABSTRACT FROM AUTHOR]

In this study, the different dosage of laminarin groups (normal fodder group (Control), normal fodder add 1 g laminarin group (AL1), normal fodder add 2 g laminarin group (AL2), normal fodder add 4 g laminarin group (AL4) and normal fodder add 8 g laminarin group (AL8)) were set. After 45 days, the growth, enzyme activity and immune‐related gene were measured to assess the effect of feeding laminarin on Ictalurus punctatus. Laminarin (4 g dosage) can significantly increase body length (p <.05) and BMI (body mass index) value in 8 g dosage group (p <.05). Lysozyme, acid phosphatase, aspartate aminotransferase, catalase, superoxide dismutase, pepsin, amylase and trypsin activity are increased with the dosage of laminarin increasing, and have a significant difference at the dosage of 4 g and 8 g group (p <.001). However, the serum cortisol content, alkaline phosphatase and lipase activity have a significant inhibition in high‐dosage groups (p <.001). The expression of TLR5 gene in group of 4 g dosage was significantly upregulated (p <.05). The expression of SOCs gene in groups of 2 g (p <.05), 4 g (p <.001) and 8 g (p <.001) dosage was significantly downregulated. The expression of IGF2 and STAT5 genes was significantly upregulated in high‐dosage laminarin groups. Generally, larminarin enhances the immune response, reduces the rate of energy decomposition required and improves food absorption. This shows that the larminarin improves the physical quality and increases productivity of I. punctatus, and offers great potential for application in the aquaculture industry. [ABSTRACT FROM AUTHOR]

The high-order tilted asymmetric surface grating (TASG) is firstly investigated and applied to quantum cascade lasers (QCLs) here. Due to the tilted grating of 16°in the middle of ridge, the longitudinal mode loss difference of TASG is about 4% higher than that of the uniform grating (UNG), which results in better single mode selectivity in reflectance spectrum. Further experimental results indicate that the TASG distributed feedback (DFB) QCL maintains more stable single longitudinal mode performance with increasing the current and temperature because that TASG have a larger longitudinal mode loss difference. While the UNG QCL with the equivalent coupling coefficient exhibits mode jumping. Hence, the drift coefficient of 0.311 nm/K of TASG QCL is 60% lower than that of the UNG device and the maximum side-mode suppression ratio (SMSR) achieves 30 dB. The characteristic temperature of TASG QCL device is 36K, which is higher than 32K of UNG device. This study showcases the feasibility and superiority of the TASG in mid-infrared active devices. • High-order tilted asymmetric surface gratings are proposed. • Effective duty cycle and loss difference of tilted asymmetric grating are large. • Optical confinement factor is responsible for the abrupt coupling coefficient. • Device maintains good single longitudinal mode performance and no mode jump. [ABSTRACT FROM AUTHOR]

This article presents an input-series output-equivalent-parallel (ISOEP) multi-inverter system for high-power wireless power transfer (WPT), where preisolation dc/dc converter is concealed to achieve higher efficiency, and the number of inverter modules is allowed to be flexibly adjusted to satisfy different voltage and power requirements. The main contribution of this article is to analyze the possible factors that may lead to voltage unbalance in the series inverters of the proposed ISOEP multi-inverter topology. At the same time, the dividing capacitor suppression and duty cycle adjustment are adopted to achieve input voltage sharing. To confirm and validate the effectiveness and merit of the proposed WPT system, a prototype consisting of three inverters connected in series was designed, built, and tested. Experiments show that the proposed topology achieved both high-power output and effective input voltage sharing. The maximum output power is 38.37 kW with an efficiency of 89.31%. [ABSTRACT FROM AUTHOR]

The wireless power transfer (WPT) system is an interesting alternative for charging electric vehicles and has a broad prospect in public transport applications. In order to obtain optimal power transfer, the existing WPT systems often require perfect alignment between the primary and secondary coils, which can hardly be met in real applications. To relieve this problem and achieve the goal of free positioning, a one-to-multiple WPT topology being able to improve the misalignment tolerance at the secondary side is proposed in this letter. Based on circuit analysis and MATLAB simulation, a mixed mode is proposed to obtain a steady power transfer with horizontal misalignment. To verify the proposed topology, a one-to-two WPT system with a three winding transformer is developed in this letter, showing that proposed topology with the mixed mode can significantly increase the positioning adaptability. [ABSTRACT FROM AUTHOR]

A three-dimensional unsteady thermal-flow coupling model for the modified chemical vapor deposition (MCVD) process is established in this work, and further the influence factors on the temperature field are extensively investigated. The results indicate that the axial wall temperature is significantly influenced by the maximum heat flux and torch speed, while the tube rotation speed and gas flowrate effects can be ignored. Generally, the temperature difference between inner and outer walls always exists, and can reach a maximum value of 169 K. Specially, it is also found that the circumferential surface temperature distribution of quartz tube appears non-uniform under the heating of oxy-hydrogen torch because of helical effect, and it is sensitive to the tube rotation speed. With increasing of the tube rotation speed, the maximum temperature difference of outer wall is reduced from 324 K to 249 K, and the maximum temperature difference of inner wall is reduced from 164 K to 65 K. Finally, the simulation results could provide theoretical basis for optimizing the operation parameters such that a uniform temperature field can be obtained to fabricate the optical fiber with high quality. [ABSTRACT FROM AUTHOR]