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With the success of generative adversarial networks (GANs) on various real-world applications, the controllability and security of GANs have raised more and more concerns from the community. Specifically, understanding the latent space of GANs, i.e., obtaining the completely decoupled latent space, is essential for applications in some secure scenarios. At present, there is no quantitative method to measure the decoupling of latent space, which is not conducive to the development of the community. In this article, we propose two methods to measure the sensitivity of latent dimensions: one is a sequential intervention method, and the other is an optimization-based method that measures the sensitivity in both the value and the direction. With these two methods, the decoupling of latent space can be measured by the sparsity of the sensitivity vector obtained. The effectiveness of the proposed methods has been verified by experiments on the representative GANs. Code will be available at https://github.com/iceli1007/latent-analysis-of .

In order to better handle the coupling effects when controlling multiple-input multiple-output (MIMO) systems, taking the decentralized control structure as the basis, this paper proposes a sparse control strategy and a decoupling control strategy. Type-1 and type-2 Takagi-Sugeno (T-S) fuzzy models are used to describe the MIMO system, and the relative normalized gain array (RNGA) based criterion is employed to measure the coupling effects. The main contributions include: i). compared to the previous studies, a manner with less computational cost to build fuzzy models for the MIMO systems is provided, and a more accurate method to construct the so-called effective T-S fuzzy model (ETSM) to express the coupling effects is developed; ii). for the sparse control strategy, four indexes are defined in order to extend a decentralized control structure to a sparse one. Afterwards, an ETSM-based method is presented that a sparse control system can be realized by designing multiple independent single-input single-output (SISO) control-loops; iii). for the decoupling control strategy, a novel and simple ETSM-based decoupling compensator is developed that can effectively compensate for both steady and dynamic coupling effects. As a result, the MIMO controller design can be transformed to multiple non-interacting SISO controller designs. Both of the sparse and decoupling strategies allow to use linear SISO control algorithms to regulate a closely coupled nonlinear MIMO system without knowing its exact mathematical functions. Two examples are used to show the effectiveness of the proposed strategies.

This article presents a control linearization technique for a single-phase single-stage inverter with multiple modulation strategies. The power topology is based on an flying capacitor multi-level (FCML) inverter, and the control is based on the plant inversion. The proposed technique allows decoupling the control of the input current and the output voltage, which simplifies the control in two ways: first, the control of both variables is independent and second, the controllers are independent of the modulation applied, thanks to the plant inversion. This control is implemented in an field-programmable gate array. Since the converter operates at variable frequency, two different data acquisition alternatives are explored: sampling at variable frequency (once per switching cycle) or sampling at constant frequency (higher than the switching frequency).The proposed control is validated by simulation and experimental results with a 1-kVA prototype.

A scientific evaluation of the broad reciprocal influences between urbanization and the eco-environment in the Tibetan Plateau region is of great significance for increasing the speed and quality of urbanization as well as restoring and improving the eco-environment. Based on a thorough look at the progress made by research on interactions between urbanization and the eco-environment in the Tibetan Plateau region, this article attempts to construct a complete analytical model of the reciprocal influences that can achieve the whole process of analyzing evaluation indexes, quantifying coupling coordination, identifying coupling types, exploring decoupling paths, and predicting future trends. Using multi-scale analysis of the Tibetan Plateau and its provinces and prefecture-level units as a means of comparison, we attempt to clarify differences at different scales, identify problem areas and propose targeted improvement measures. The result shows that the urbanization evaluation indexes for the Tibetan Plateau at different scales rise in stages and that the urbanization index for Qinghai is higher than for Tibet; the changes in the eco-environment index of the two regions are also different, with a downward trend in Qinghai and a trend toward stability in Tibet, and with stratification in the eco-environment indexes of prefecture-level units; the degree of coupling coordination between urbanization and the eco-environment at different scales in the Tibetan Plateau region is increasing overall, with the type of coordination changing from uncoordinated deterioration to borderline uncoordinated deterioration, and ultimately changing into scarcely coordinated development, which basically puts the region into the logging urbanization category; and the urbanization and eco-environment indexes display a dynamic trend of alternating between strong decoupling and weak decoupling, indicating that there is a negative reciprocal influence between urbanization and eco-environment at different scales and that the phenomenon of passive urbanization is prominent. We predict that in the next 10 years, the system coupling coordination of prefecture-level units in the Tibetan Plateau region will steadily increase, but there will be significant discrepancies in the growth rates of different regions.

The power amplifier (PA) behavior models based on deep neural networks (DNNs) have been widely used. However, it is challenging to balance the network depth, the number of neurons, and the combination of input terms. Accordingly, how to obtain a suitable DNN model becomes a problem. This letter proposes a method to acquire the corresponding optimal DNN structure balancing multiple aspects for a PA by decoupling the relationship of the three factors mentioned above. The algorithm first chooses the optimal input combinations, and then utilizes the inputs to select the DNN structure with excellent capability to fit nonlinear functions and fewer coefficients. Experimental results verify that the DNN model acquired by the algorithm proposed can maintain superior performance with low complexity.

This article proposes modeling and two controller design techniques for a triple active bridge (TAB) three-port dc–dc converter comprising of fuel cell, battery, and load. The sources are integrated, employing boost interleaved full-bridge converters in order to get smoother source current profile. The converter is a nonlinear multi-input multi-output (MIMO) system with a large number of control variables. Moreover, a high degree of coupling among the control variables makes its modeling and control system design quite cumbersome and complex. To overcome the complexity of analysis in a higher order system like TAB, a generalized frequency-domain modeling technique is introduced in this article. A new decoupling matrix-based proportional-integral controller design method is also proposed. It reduces the design complexity and improves the system dynamic performance (lower settling time, overshoot/undershoot in the controlled variables) in comparison to similar three-port converters reported in the literature. Further, the performance of the proposed controller is compared by simulation with another popular MIMO system controller design technique, namely the state feedback control. A 1-kW laboratory prototype is built and tested to verify the system performance during dynamic load changes, source current variation, and battery charging/discharging operation.

The performance of an optical system with sensitive line-of-sight (LOS) is influenced by rotational vibration. In view of this, a design methodology is proposed for a passive vibration isolation system in an optical system with sensitive LOS. Rotational vibration is attributed to two sources: transmitted from the mounting base and generated by modal coupling. Therefore, the elimination of the rotational vibration caused by coupling becomes an important part of the design of the isolation system. Additionally, the decoupling conditions of the system can be obtained. When the system is totally decoupled, the vibration on each degree of freedom (DOF) can be analyzed independently. Therefore, the stiffness and damping coefficient on each DOF could be obtained by limiting the vibration transmissibility, in accordance to actual requirements. The design of a vibration isolation system must be restricted by the size and shape of the payload and the installation space, and the layout constrains are thus also discussed.

We present a method for seamless surface parametrization. Recent popular methods first generate a cross-field, where curvature is concentrated at singular vertices. Next, in a separate step, the surface is laid out in the domain subject to derived seamlessness constraints. This decoupling of the process into two independent problems, each with its own objective, leads to suboptimal results. In contrast, our method solves both problems together using domain variables. The key ingredient to the robustness of our method is a rounding strategy based on local estimation. The insight is that testing a small patch to decide between two likely possibilities is a good estimator. Most distortion measures can be used with our method, which get minimized consistently throughout the pipeline. Our method also enables feature alignment, as well as alignment to principle curvatures, and isotropic and anisotropic scaling.

In real practice, the construction of drainage wells is always accompanied by smear effect, whereas it is scarcely discussed in the field of unsaturated soils. In this case, on the basis of the widely-accepted consolidation theory for unsaturated stratum by Fredlund, this paper studies the equal strain consolidation characteristics of unsaturated foundation with drain wells affected by smear effect under time-varying loading (exponential loading as a case). Firstly, with the consideration of corresponding boundary conditions, the governing equations under the equal strain hypothesis are obtained. Afterwards, decoupling process, constant variation method and Fourier series expansion theory are utilized to get the analytical solutions, which are then verified to be credible by means of the finite difference method. Finally, the consolidation patterns of the drainage well foundation modeling in unsaturated soils are studied against hydraulic coefficients, modeling sizes and loading parameter. It reveals that the smear effect can significantly restrain the consolidation process of the foundation by drain wells, while different parameters will lead to various results.

Computational simulation provides an effective way of understanding the disordered structure and structure-property relationships for metallic glass systems. Here, we systematically investigated the finite-size effects of the static structure and dynamical behaviors in a three-dimensional Cu50Zr50 model metallic glass via classical molecular dynamics (MD) simulations. It was found that the local structure is insensitive to the system size while the dynamical properties present evident finite-size effects. The decoupling between local structure and relaxation dynamics in the investigated supercooling emerges when the system contains less than ~2000 atoms. However, the collapse can be observed between the structural relaxation time and the dynamical heterogeneity for different sized systems across the whole range of our investigation. Our results support the intrinsic link between the structural relaxation time and dynamic heterogeneity and reveal the critical simulated system size for representing the structural origins of dynamics in bulk metallic glass with ignorable surface effects.

Through the use of qualitative analysis, this paper examines the diverse tensions that social entrepreneurs have to deal with in their daily business activity. By using paradox theory and the hybrid organization model as a framework for analysis, we have found three principle causes of tension among social entrepreneurs: social vs economic sustainability; work vs family life; and resistance to change vs innovation. The results show the way in which social entrepreneurs in hybrid organizations resolve these conflicting tensions, usually through a selective coupling strategy, which is eventually complemented with alternative approaches such as compromising or decoupling. Social entrepreneurs tend to focus on one aspect of the tension and deal with it individually, which makes it more manageable. Change and innovation are the triggers for using strategies other than selective coupling, such as compromising or decoupling. When the level of tension rises further, compromising is then used. Decoupling is the last option chosen by social entrepreneurs, and is used only in cases where resolution is not possible with the other two strategies mentioned.

In this paper, the dynamic coupling between the wind turbine rotor speed recovery (WTRSR) and inertial response of the conventional virtual synchronous generator (VSG) controlled wind farms (WFs) is analyzed. Three distinguishing features are revealed. Firstly, the inertial response characteristics of VSG controlled WFs (VSG-WFs) are impaired by the dynamic coupling. Secondly, when the influence of WTRSR is dominant, the inertial response characteristics of VSG-WFs are even worse than the condition under which WFs do not participate in the response of grid frequency. Thirdly, this phenomenon cannot be eliminated by only enlarging the inertia parameter of VSG-WFs, because the influence of WTRSR would also increase with the enhancement of inertial response. A decou-pling scheme to eliminate the negative influence is then proposed in this paper. By starting the WTRSR process after inertial response period, the dynamic coupling is eliminated and the inertial response characteristics of WFs are improved. Finally, the effectiveness of the analysis and the proposed scheme are verified by simulation results.

Water holdup measurement in multiphase flow with varying water salinity is a significant and challenging research topic. In this study, a new microstrip-antenna-based microwave transmission sensor (MAMTS) and a new decoupling method are proposed to achieve salinity independent measurement of water holdup in oil–gas–water slug flows. The MAMTS is formed by two identical microstrip antennas whose configurations are optimized and determined by the formula computation and the finite element method. Owing to the optimum design of the antenna configuration and the determination of the suitable working frequency, the amplitude attenuation and phase shift simultaneously measured by the MAMTS are both sensitive to the water holdup and water conductivity. To overcome the existing problem that the salinity affects the water holdup measurement only using amplitude attenuation or phase shift, a new decoupling method based on the simultaneous measurement of amplitude attenuation and phase shift is proposed. The measurement characteristics of the MAMTS to different flow structures with different water holdups and the influence of the water conductivity change are studied by the static experiments. Finally, the performance of the MAMTS and the decoupling method are evaluated in the experiment of oil–gas–water slug flows. The experiment results indicate that the amplitude attenuation and phase shift both can effectively sense the information of water holdup and water conductivity. However, they present different measurement characteristics to different flow structures. By taking the flow structures into consideration and using the new decoupling method, the water conductivity can be derived online and salinity independent water holdup measurement in oil–gas–water slug flow can be achieved.

In this study, an omnidirectional inverted pendulum (ODIP) is controlled based on a dual Takagi–Sugeno (TS) fuzzy control scheme. The ODIP is a handmade system. It contains two subsystems. The lower mechanism system includes a brushless rim motor, a system platform, batteries, and an encoder. The upper mechanism system is mainly composed of a circuit system, a motor fixed platform, a motor, and a flywheel. The proposed controller combines two fuzzy control approaches for ODIP system control with disturbances and uncertainties. The core of the ODIP operating system is an embedded controller, which executes real-world control processes. Moreover, to address the coupling problem, the shafts of the two motors are oriented in orthogonal directions. Then, the two fuzzy controllers can be designed independently without coupling. In the proposed controller, the Takagi–Sugeno fuzzy model is adopted for fuzzy modeling of the ODIP. The conception of parallel distributed compensation (PDC) is utilized to develop fuzzy control from TS fuzzy models. The format of linear matrix inequalities (LMIs) can formulate sufficient conditions. The main contributions of this study are (1) the implementation of an ODIP and (2) the application of the proposed dual Takagi–Sugeno (TS) fuzzy control scheme for real-time control of the ODIP. Finally, the efficiency of the proposed control scheme is illustrated by the experimental results presented in this study.

Self-sustained oblique detonation behind a spherical projectile formed in a non-uniform H2/O2/Ar mixture was numerically investigated. A hypersonic combustible mixture flow around a 4.76 mm diameter body was modeled to be flowing at 2500 m/s and 100 kPa. The concentration gradient was prescribed applying the Gaussian distribution to hydrogen concentration. Axisymmetric Euler equations including a detailed kinetics of 9 species and 27 elementary reactions were solved with an explicit 2nd-order time integration scheme combined with point implicit method for chemical reaction. Oblique detonation was always obtained when the mixture on the centerline was stoichiometric, as it is for a uniform mixture, and a broader range of equivalence ratio could sustain oblique detonation far from the sphere. Local detonation angle was revealed to reasonably match Chapman-Jouguet analytical solutions with a minor difference attributed to curvature, less reactive composition, and the concentration gradient. Also, a strongly fuel-rich region encountered decoupling of the shock-flame, in which an abrupt deflection of the shock front appeared. These decoupling phenomena can be attributed to a slower kinetics of a less reactive mixture. All of interesting findings in this study will also benefit understanding of various form of detonation in non-uniform mixture taking advantage of the analogy between them.

Machining distortion caused by residual stresses is one of the major challenges in the production of thin-walled monolithic parts, which are widely used in the aerospace industry. This distortion often results in large deviations in form and position outside the tolerance requirements of the part. Time-consuming and cost-intensive running-in processes or manual reworking are therefore necessary to meet the tolerances of the parts. Current research mainly uses the finite element method (FEM) to predict machining distortion caused by residual stresses. However, the disadvantage of the FEM is the high manual effort required to generate a computational mesh of the in-process workpiece (IPW). Moreover, the FEM demands a very fine mesh, which has to be frequently updated by remeshing, to be in good agreement with the IPW. This leads to high computation times overall. In this paper, a novel machining distortion prediction method based on the Finite Cell Method (FCM) is presented. A major advantage of FCM compared to the established FEM in the context of milling simulation is the decoupling of the computational mesh and the IPW geometry, which allows for analysis updates of the modified IPW due to material removal, without the need for expensive re-meshing. Thus, the time complexity of the simulation can be reduced significantly. The milling process of a thin-walled part made of Ti-6Al-4V was considered to demonstrate the overall simulation approach.

In a material prone to a nematic instability, anisotropic strain in principle provides a preferred symmetry-breaking direction for the electronic nematic state to follow. This is consistent with experimental observations, where electronic nematicity and structural anisotropy typically appear hand-in-hand. In this work, we discover that electronic nematicity can be locally decoupled from the underlying structural anisotropy in strain-engineered iron-selenide (FeSe) thin films. We use heteroepitaxial molecular beam epitaxy to grow FeSe with a nanoscale network of modulations that give rise to spatially varying strain. We map local anisotropic strain by analyzing scanning tunneling microscopy topographs, and visualize electronic nematic domains from concomitant spectroscopic maps. While the domains form so that the energy of nemato-elastic coupling is minimized, we observe distinct regions where electronic nematic ordering fails to flip direction, even though the underlying structural anisotropy is locally reversed. The findings point towards a nanometer-scale stiffness of the nematic order parameter., Here, in contrast to previous observations in Fe-based superconductors, scanning tunnelling microscopy of strain-patterned FeSe thin films reveals a local decoupling of electron nematicity and structural anisotropy, pointing towards a stiffness of the nematic order parameter at the nanoscale.

This note is concerned with global stabilization of linear systems subject to input saturation and time delays. Based on the Luenberger canonical form, two new decoupling methods are proposed. For the decoupled system, according to some special canonical forms, we propose two control laws for systems with input time-delays and systems with input saturation and time-delays, and give explicit conditions to ensure the global stability of the closed-loop system. Two special canonical forms contain time delays in input and state vectors, which is essential in recursive design. In addition, for the system subject to input saturation and time-delay, we introduce some free parameters when designing the controller, which can improve the instantaneous performance of the closed-loop system. Finally, the proposed approach is applied on the multi-agent system to design global stabilizing controllers and the effectiveness of the proposed controllers are illustrated by numerical simulations.

In this paper, the decentralized economic scheduling problem of multi-micro grid is studied on the basis of considering the uncertainty of wind power and photovoltaic output. Firstly, in order to meet the independent and autonomous requirements of decentralized scheduling, the decoupling method of public connection points, which is more suitable for the multi-micro grid structure, is adopted to partition the multi-micro grid regions. Then, considering the uncertainty of wind-wind output, a decentralized economic dispatching model of multi-micro grid with robust optimization participation is proposed to minimize the economic cost. Finally, the improved Alternating direction method of multipliers is used to solve the decentralized economic scheduling problem of multi-micro grid. The feasibility and superiority of the improved algorithm are verified by simulation test and comparative analysis. The decentralized autonomy of multi-micro grid scheduling can be realized, and the random uncertainty of landscape can be effectively dealt with.

The laryngeal tissue is sensitive to the operating force of the surgical instrument in the tissue resection. A large operating force may cause rupture of the vocal cords and surrounding tissues, which affect the patients’ normal voice. However, a small operating force cannot achieve tissue resection. Based on experience, surgeons can estimate the operating force in the process of using a surgical robot. The estimated operating force is usually inaccurate, which may increase the failure rate of surgery. Therefore, a fiber Bragg gratings (FBGs)-based 3-D force sensing sensor for the surgical robot, which consists of two flexible hinges in series is designed. And the force sensing model is constructed through mechanical modeling and temperature decoupling algorithm. The experimental results show that the designed sensor has a resolution of 14.3, 9.7, and 13.8 mN for external forces ${F_{x}},{F_{y}}$ , and ${F_{z}}$ , respectively. The proposed mechanism has good axial and lateral force sensing capability. In the experiment of simulating diseased tissue with the silicone-steel ball, the designed sensor can accurately determine the position of the steel ball, which verifies the effectiveness and feasibility of the designed FBG sensor in force perception.

The design of multiple input multiple output (MIMO) antennas using decoupling networks (DNs) often requires optimization and is computationally time consuming when using full electromagnetic solvers. To reduce the computational cost, the use of space mapping (SM) techniques in MIMO antenna design is proposed. Two approaches are considered for developing the necessary SM coarse model for MIMO antennas. The first is based on conventional DNs while the second uses a novel application of the internal multiport method (IMPM). Various parameter extraction (PE) techniques for SM techniques suitable for MIMO DN design are also developed. Two MIMO antennas are optimized to demonstrate the effectiveness and efficiency of the proposed schemes. Compared to conventional optimization, the proposed schemes improve the optimization speed by factors of approximately 20 and importantly, meeting the antenna design specifications for all examples.

Nonlinear dynamic systems are often approximated by a Volterra series, which is a generalization of the Taylor series for systems with memory. However, the Volterra series lacks physical interpretation. To take advantage of the Volterra representation while aiming for an interpretable block-oriented model, we establish a link between the Volterra representation and the parallel Wiener-Hammerstein model, based on decoupling of multivariate polynomials. The true link is through a constrained decoupling model with (block-)Toeplitz structure on the factors and sets of identical internal branches. The solution of the modified decoupling problem then reveals directly the parameters of the parallel Wiener-Hammerstein model of the system. However, due to the uniqueness properies of the plain decoupling algorithm, even if the structure is not imposed, the method still leads to the true solution (in the exact case).

In this paper, a robust adaptive fuzzy proportional-derivative inverse dynamics decoupling control scheme with fuzzy-based linear extended state observer (FLESO) is presented and applied to the trajectory tracking control of a two degree-of-freedom (2-DOF) spherical motion mechanism (SMM). The dynamics of the SMM has the characteristics of multivariable nonlinearity, uncertainties and strong coupling. Uncertainties like the modeling errors and external disturbances affect the tracking performance, and coupling increases the difficulty of controller design and reduces the tracking precision. Therefore, a novel hybrid control scheme that is composed of a fuzzy proportional-derivative (FPD) feedback control with varying gains, inverse dynamic model-based feed-forward decoupling term, FLESO with varying bandwidth, and robust term is developed. The novel control strategy combines the advantages of simplicity and easy design of the FPD control, the effectiveness of the FLESO to handle the modeling errors and external disturbances, and the robustness of the robust term to estimation errors of the FLESO. First, introduce the structure of the SMM and establish the dynamic model. Second, the feed-forward decoupling principle is derived based on the inverse dynamic model. Then the FPD control with two-inputs and two-outputs is designed, whose rule base is derived by the phase plane method. The linear extended state observer is designed, whose bandwidth is tuned via the fuzzy logic system. Furthermore, the asymptotic stability of the proposed controller is proved by the Lyapunov theorem. Finally, the high tracking performance of the proposed controller is validated via both simulation and experiment results.

Quantitative phase imaging (QPI) with its high-contrast images of optical phase delay (OPD) maps is often used for label-free single-cell analysis. Contrary to other imaging methods, sensitivity improvement has not been intensively explored because conventional QPI is sensitive enough to observe the surface roughness of a substrate that restricts the minimum measurable OPD. However, emerging QPI techniques that utilize, for example, differential image analysis of consecutive temporal frames, such as mid-infrared photothermal QPI, mitigate the minimum OPD limit by decoupling the static OPD contribution and allow measurement of much smaller OPDs. Here, we propose and demonstrate supersensitive QPI with an expanded dynamic range. It is enabled by adaptive dynamic range shift through a combination of wavefront shaping and dark-field QPI techniques. As a proof-of-concept demonstration, we show dynamic range expansion (sensitivity improvement) of QPI by a factor of 6.6 and its utility in improving the sensitivity of mid-infrared photothermal QPI. This technique can also be applied for wide-field scattering imaging of dynamically changing nanoscale objects inside and outside a biological cell without losing global cellular morphological image information., Quantitative phase imaging: Sensitivity boost A scheme that significantly enhances the sensitivity of quantitative phase imaging (QPI) could help benefit a range of applications in optical imaging and sensing. Keiichiro Toda and co-workers from The University of Tokyo, Japan call their scheme adaptive dynamic range shift quantitative phase imaging (ADRIFT-QPI). It works by “cancelling out” the sample-induced large optical phase delays (OPDs) by wavefront shaping and measuring the remaining small OPDs with a boosted sensitivity by dark-field microscopy. The team tested the approach with mid-infrared photothermal QPI measurements and confirmed a ~6.6 times sensitivity improvement. It is thought that the approach may also prove beneficial for detecting movement of nanoscale particles such as exosomes or viruses inside and outside a biological cell and slight OPD changes accompanied by the intracellular molecular dynamics.

This paper investigates the observer-based dissipative control problem for a class of discrete-time Markovian jump fuzzy systems under mismatched fuzzy basis functions. In the practical implementation of the observer-based control scheme, the system state variable can be measured with uncertainties and disturbances, which acts as a factor that prevents accurate measurement of the premise variable. Thus, in this case, it is necessary to explore the phenomenon that the system premise variable cannot be reflected in the design of the fuzzy-basis-dependent observer and controller. In response to this need, this paper proposes a method to deal with the mismatch phenomenon in the observer-based stabilization problem of MJFSs by devising a two-step approach to solve the inherent decoupling problem and by providing a useful relaxation technique for the error of mismatched fuzzy basis functions.

The molecular-level structure and dynamics decide the functionality of solvent media. Therefore, a significant amount of effort is being dedicated continually over time in understanding their structural and dynamical features. One intriguing aspect of solvent structure and dynamics is heterogeneity. In these systems, the dynamics follow , where p is the measure of viscosity decoupling. We analytically predicted that in such cases, the Stokes–Einstein relationship is modified to due to microdomain formation, and the second term on the right-hand side leads to viscosity decoupling. We validated our prediction by estimating the p values of a few solvents, and they matched well with the literature. Overall, we believe that our approach gives a simple yet unique physical picture to help us understand the heterogeneity of solvent media.

International audience; This paper presents an Unknown Input Observer (UIO) for a class of Lipschitz nonlinearsystems affected by disturbances/faults and having slow internal dynamics after the decoupling ofthe Unknown Input (UI). Thanks to the Asymptotic Decoupling notion, the constraint related to theinternal dynamics is bypassed. The nonlinear UIO presents time varying dynamics that allows toobtain the Asymptotic Decoupling notion. It will be shown that the proposed approach allows toenhance the convergence rate of the UIO. The convergence enhancement of the state estimation erroris demonstrated via a Lyapunov analysis and the established conditions are expressed in terms of LinearMatrix Inequalities. Finally, simulations will be given in order to illustrate the proposed approach withsome comparisons to existing techniques.

This paper is concerned with the issue of fault-tolerant anti-synchro-nization control for chaotic switched neural networks with time delay and reaction-diffusion terms under the drive-response scheme, where the response system is assumed to be disturbed by stochastic noise. Both arbitrary switching signal and average dwell-time limited switching signal are taken into account. With the aid of the Lyapunov-Krasovskii functional approach and combining with the generalized Ito formula, sufficient conditions on the mean-square exponential stability for the anti-synchronization error system are presented. Then, by utilizing some decoupling methods, constructive design strategies on the desired fault-tolerant anti-synchronization controller are proposed. Finally, an example is given to demonstrate the effectiveness of our design strategies.

Artificial tactile sensing in next-generation robots requires the development of flexible sensors for complicated tactile force measurements in both normal and tangential directions. A variety of microstructures have been proposed to be integrated with material development for the enhanced performance of the devices. However, there remains a great challenge in this field on how to decouple contact forces in spatially arbitrary directions with the electric signal readouts. The fundamental correlation between contact force sensing and the microstructure deformations is still largely unknown. Here, we report a new method of arbitrary force deconvolution and sensitive detection of flexible contacts by a porous dielectric elastomer-based force (PDiF) sensor. Decoupling the complicated nonlinear mathematic problem reveals a critical synergy in the porous elastomer between the electrical property enhancement and the geometrical deformations induced by arbitrary contact forces. Proof-of-concept applications in flexible tactile sensing have been demonstrated with the PDiF sensors, including surface roughness discrimination, slippage detection, and real-time force mapping in handwriting. It creates an avenue for flexible sensing of the complicated contact forces with microstructure-embedded elastomeric materials.

Modern turbofan engines are presented with nonlinearity, strong couplings and disturbances. These features have brought challenges not only to obtain a precise model but to design control systems. Many modern control methods rely on accurate model information and PID is difficult to meet the increasingly high control requirements. Active disturbance rejection control (ADRC) is known for its excellent performance on self-decoupling, disturbance rejection, simplicity and low precision requirements for the model. For the couplings and disturbances of turbofan engines, a modified method, ADRC based on feedforward inverse system, is presented. The simulation shows that the modified method has superior dynamic performance than ADRC and SIMC-PID in setpoint tracking, disturbance rejection and decoupling ability. Moreover, the modified method is more robust than ADRC and SIMC-PID.

Additive manufacturing (AM) machines have developed more rapidly than standardized frameworks needed for the qualification of their geometric capabilities. While some manufacturer-specific methods exist to test capabilities and perform some calibration tasks, standardization efforts have only recently been undertaken in the form of ISO/ASTM 52902. In this study, the recommended methodology prescribed by the standard was implemented by building geometric artifacts with a laser powder bed fusion (LPBF) system and performing dimensional inspection with a coordinate measurement machine (CMM), amongst other methods. Typical dimensional capabilities of the LPBF system are identified and commentary is made on applying metrology methods, detecting geometric error, and diagnosing base causes in the LPBF system. In doing so, favored metrology practices and measurement analysis methods auxiliary to the standard are proposed. Artifact measurements were used to characterize beam positioning error and beam offset error. Methods for decoupling the effects of error sources are proposed. Difficulties in the inspection of AM components are identified, and the effects of various CMM measurement strategies are evaluated. Insights on the application of the new standard are presented, along with commentary as to its fitness for the LPBF process.

Extreme ambient temperatures cause electric vehicles’ batteries to deteriorate and have a major impact on driving range, which is a barrier for mass production of electric vehicles. Recently, much research on the optimized temperature of the battery and energy management of an electric vehicle has been conducted, but the vehicle-level optimization (i.e., vehicle speed and position optimizations) has not yet been considered together. This paper proposes a hierarchical model predictive control structure for vehicle-level and electric powertrain-level optimizations simultaneously. Specifically, using vehicle communication technologies to forecast future traffic, the required vehicle traction power coupled with battery dynamics can be predicted, and this predicted traction power is used when designing the thermal control of the battery. Furthermore, the computationally tractable control could be designed for real-time application through decoupling the vehicle and battery dynamics. The simulation results under highway and urban driving conditions show the efficacy of our approach by comparing the battery energy consumption with that of the baseline methodology, i.e., conventional control.

Design of tracking controller for quadrotor is an important issue for many engineering fields such as COVID-19 epidemic prevention, intelligent agriculture, military photography and rescue nowadays. This study applies the feedback linearized method and linear quadratic regulator (LQR) method using particle swarm optimization (PSO) to analysis and stabilize the highly nonlinear quadrotor system without applying any nonlinear function approximator that includes neural network approach and fuzzy approach. The article proposes a new method based on the firstly proposed convergence rate formula to achieve the optimal weighting matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally achieved via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This article firstly uses the convergence rate formula of the nonlinear system as the fitness function of LQR approach by using PSO to take the place of the trial and error method. The generalities and implications of proposed approach are globally valid, whereas the Jacobian linearized approach is locally valid due to the Taylor expansion theorem. In addition to these two major achievements, the significant innovation of the proposed method is to possess “simultaneously” additional performances including the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization and globally exponential stability performances. Comparative examples show that the convergence rate with our proposed optimal controller using the PSO algorithm is larger than the fuzzy method, and better than the singular perturbation method with high-gain feedback.

While metamaterial aperture imaging systems do not require mechanical scanning equipment or complex components by employing a spatially variant radiation field, they require large amount of data and many computations. In this paper, we deduce the contribution of the resonator to the radiation fields of the metamaterial aperture. We propose a fast range decoupling algorithm that can improve the data processing speed and obtain real-time images of far-field scenes. The algorithm decomposes the scene into numerous range cells, drastically reduces the range of interest, and reconstructs the scene in parallel. Simulation results show that computational cost is significantly decreased and image quality is maintained.

In the context of dynamic decoupling problems, engineering dynamics has long held modal analysis as an exemplar. The method allows the exact decomposition of linear multi-degree-of-freedom (MDOF) systems into single-degree-of-freedom (SDOF) oscillators, thus simplifying complex dynamic problems considerably. However, modal analysis is very much a linear theory; if applied to nonlinear systems, the decoupling property (among others) is lost. This unfortunate situation has led to numerous attempts to formulate workable nonlinear versions of the theory. The current paper extends previous work by the authors in using machine learning methods to learn nonlinear modal transformations on measured data, based on the premise that any latent modal variables should be statistically independent. Unlike previous work, the transformation here exploits the recent development of normalising flows in constructing the required transformations. The new approach is shown to overcome a number of the problems in the original approach when demonstrated on a simulated nonlinear system.

We study nonlinear control systems x˙(t) = f(x(t)) + g(x(t))u(t) + d(x(t))w(t), y(t) = h(x(t)), where f,g,d,h are polynomial functions. The output y is called decouplable from disturbances if there exists a polynomial state feedback u(t) = α(x(t)) + β(x(t))v(t) with β as an invertible matrix, which renders the output y invariant under disturbances. The question whether this is possible leads to the concept of controlled invariant modules. We give algebraic characterisations to decide whether an output is invariant or decouplable and present algorithms to constructively verify these criteria by symbolic computational methods.

Black-box model structures are dominated by large multivariate functions. Usually a generic basis function expansion is used, e.g. a polynomial basis, and the parameters of the function are tuned given the data. This is a pragmatic and often necessary step considering the black-box nature of the problem. However, having identified a suitable function, there is no need to stick to the original basis. So-called decoupling techniques aim at translating multivariate functions into an alternative basis, thereby both reducing the number of parameters and retrieving underlying structure. In this work a filtered canonical polyadic decomposition (CPD) is introduced. It is a non-parametric method which is able to retrieve decoupled functions even when facing non-unique decompositions. Tackling this obstacle paves the way for a large number of modelling applications., Accepted for presentation at the 19th IFAC Symposium on System Identification (SYSID 2021)

In this article, we study long time dynamics for defocusing cubic nonlinear Schrodinger equations (NLS) on three dimensional product space. First, we apply the decoupling method in Bourgain and Dem...

As the basis of Tactile Internet, remote haptic display has been made possible with the development of ultra-reliable low-latency communication in 5G. In this study, edge learning is employed to enable realistic haptic display and stable remote communication. We propose a double-loop control algorithm, which merges decoupling and PID neural network, for magnetic field generation of the electromagnetic haptic device. In addition, a supervised bidirectional LSTM network is constructed for online haptic prediction during remote interaction, thus complementing the missing haptic data on account of time delay and packet loss in network communications. Experiments have been conducted on the built remote haptic display system, where data streams from sensors are gathered, stored, and forwarded in real time. The results show that dynamic and accurate haptic display is achieved through our magnetic field control algorithm for the haptic device, and the error of haptic prediction by step is less than 0.01N. The conclusion is that the service sustainability of remote haptic display can be guaranteed by edge learning effectively.

This letter proposes a novel primary-side parameter estimation method for bidirectional inductive power transfer (BD-IPT) systems. This method can estimate mutual inductance at startup and monitor secondary-side phasor information continuously during operation. The proposed method can be used to simplify existing optimal control methods in BD-IPT systems by providing necessary feedback information without a wireless communication link. Besides, it helps to implement new control methods to further improve system performance. A current decoupling concept is utilized to estimate both the mutual inductance and secondary phasor information. A circuit model based on the superposition principle is presented to describe the current decoupling concept. The validity and accuracy of the proposed estimation method are demonstrated using the measured results of a 1-kW prototype BD-IPT system.

The transport sector is one of the major pillars of each country’s domestic and foreign economy. The different modes of transport are equally important and all have interconnections. The degree of liberalization is different for each and the degree of competition. The purpose of this article is to define the disconnection point of an order by the customer when ordering ad-hoc train paths on the Slovak Railways network because of liberalization. The aim is to find a point where the customer, or the carrier, when ordering these routes and when the initiative is already discussed by the infrastructure manager. At the end of the article, individual differences between the order of the train route in Slovakia and the Czech Republic will be outlined. In this case, it will be pointed out a completely different degree of comfort of the carrier when placing an order for a train path.

Existing methods for estimating nonlinear dynamic models are either highly computationally costly or rely on local approximations which often fail adequately to capture the nonlinear features of interest. I develop a new method, the discretization filter, for approximating the likelihood of nonlinear, non‐Gaussian state space models. I establish that the associated maximum likelihood estimator is strongly consistent, asymptotically normal, and asymptotically efficient. Through simulations, I show that the discretization filter is orders of magnitude faster than alternative nonlinear techniques for the same level of approximation error in low‐dimensional settings and I provide practical guidelines for applied researchers. It is my hope that the method's simplicity will make the quantitative study of nonlinear models easier for and more accessible to applied researchers. I apply my approach to estimate a New Keynesian model with a zero lower bound on the nominal interest rate. After accounting for the zero lower bound, I find that the slope of the Phillips Curve is 0.076, which is less than 1/3 of typical estimates from linearized models. This suggests a strong decoupling of inflation from the output gap and larger real effects of unanticipated changes in interest rates in post Great Recession. Nonlinear filtering discretization regime switching state space models DSGE models zero lower bound C11 C13 E40 E50

By comparing several cloud computing of big data network center during COVID-19, this paper proposes a new topology model, which realizes two functions of cloud computing big data center caching and big data real-time distribution In addition, cloud computing network requires higher performance than traditional application big data center, which makes the consideration of network platform construction performance different from the traditional understanding During COVID-19, we deeply understood the underlying attributes of cloud, combined with the topology model, we can realize the decoupling of cloud computing big data system, change the situation of direct connection between upstream and downstream, and have more reliable and efficient transmission of message and command big data © 2020 - IOS Press and the authors All rights reserved

A control-oriented circuit model of an inductive power transfer system is needed to design a high-performance controller. In this article, a dq synchronous reference frame model of a series–series tuned resonant inductive power transfer system is proposed. A single-phase dq modeling technique for a resonant converter is used to derive the model. The proposed control-oriented model clearly demonstrates the cross- and mutual-coupling between the state variables of the wireless power transfer system. It is interesting to note that the proposed model is similar to a dq synchronous reference frame model of a three-phase induction motor. The well-known controller design methods, such as proportional-integral controller design, manipulated input decoupling, disturbance input decoupling, state observers, and disturbance observers, etc. can be extended to the inductive power systems with the proposed model. The large- and small-signal responses of the transmitter current versus its input voltage are identified. Considering the frequency response, a new transmitter current controller that has a control bandwidth of 500 Hz is designed to show the validity of the proposed model. The proposed model and the controller are evaluated using simulated and experimental results. The simulated open- and closed-loop frequency responses of the model agree well with the theoretical bode plots of them. Also, the experimental results show that the proportional-integral with feedforward input controller regulates the transmitter current within 1 ms for three conditions: a step change of the reference, a step change of the load, and a step change of the mutual-coupling.

The focus of this paper is to obtain anisotropic spherically symmetric solutions by means of gravitational decoupling in the background of self-interacting Brans-Dicke theory. We introduce minimal geometric deformation in the radial metric component to decouple the field equations into two arrays. The first set, governed by the seed source, is determined through metric functions of isotropic solution (Heintzmann/Tolman VII spacetimes) while the second set is solved by imposing two constraints on the anisotropic source. The unknown constants are evaluated via matching conditions at the stellar boundary. We investigate the effects of massive scalar field as well as decoupling parameter on the physical structure of anisotropic models and check them for viability through energy conditions. It is concluded that the anisotropic solutions obtained through constraint I are well-behaved for selected values of the decoupling parameter. For the second constraint, the extended Heintzmann solution is viable but anisotropic Tolman solution does not comply with dominant energy condition for higher values of the decoupling parameter.

In inductive power transfer (IPT) applications, load-independent output has become more popular. This letter proposes a reconstructed S-LCC topology with dual-type outputs, which can realize constant current (CC) output and constant voltage (CV) output simultaneously. The proposed reconstructed S-LCC topology uses the same number of passive components, inductor or capacitor, as conventional S-LCC topology while provides dual-type outputs, which results in reduced cost and size. Such dual-type outputs could be treated as two independent output ports without using any decoupling methods, which can simplify the parameter design significantly. The circuit model of the proposed reconstructed S-LCC topology is analyzed in detail. Comparison between the proposed reconstructed S-LCC topology and multicoil structure is performed. Experimental prototypes with 300V/1.5 kW CV output and 10A/1 kW CC output are fabricated to verify the feasibility of the proposed topology.

In this paper, a class of discrete-time singularly perturbed switched systems $\left ({\text {SPSSs}}\right) $ with persistent dwell-time $\left ({\text {PDT}}\right)$ switching law is firstly proposed. Using the slow-state feedback control method, sufficient conditions to ensure the global uniform exponential stability of the closed-loop PDT SPSSs are derived. Based on the aforementioned conditions, the analyses of the extended dissipative performance of the closed-loop PDT SPSSs and a preferable decoupling method deriving the mode-dependent controller gains are given for the first time. Finally, the potential practicability of the method is verified by a purely numerical example and a tunnel diode circuit model, and a convex optimization method calculating the upper bound of the singular perturbation parameter is provided.

In this article, a novel voltage-sensorless controller for the single-phase ac/dc power conversion systems with a self-adaptive power-decoupling function is proposed. Existing control strategies for achieving power decoupling usually require additional active switches or extra measurement circuits to a certain power-decoupling controller. By employing the proposed method, the decoupling voltage could be accurately evaluated with the fluctuations of the other measured state variables in the original converter, and the issues of instability and inaccuracy on power-decoupling control under transient conditions could be solved. Furthermore, the proposed control scheme involves a phase-shift modification to improve the power quality as well as achieve accurate power-decoupling reference, which could eliminate the harmonic influence on the original PFC circuit. Finally, a 1.2-kW single-phase PFC prototype using SiC-based semiconductor switching devices is tested to validate the proposed control scheme. The experimental results demonstrate that the power factor of 0.99 with total harmonic distortion around 3%, and a dc-link voltage with 5% ripple could be achieved with the proposed control strategy.

In the standard model of cosmology, the Universe began its expansion with an anomalously low entropy, which then grew dramatically to much larger values consistent with the physical conditions at decoupling, roughly 380,000 years after the Big Bang. There does not appear to be a viable explanation for this `unnatural' history, other than via the generalized second law of thermodynamics (GSL), in which the entropy of the bulk, S_bulk, is combined with the entropy of the apparent (or gravitational) horizon, S_h. This is not completely satisfactory either, however, since this approach seems to require an inexplicable equilibrium between the bulk and horizon temperatures. In this paper, we explore the thermodynamics of an alternative cosmology known as the R_h=ct universe, which has thus far been highly successful in resolving many other problems or inconsistencies in LCDM. We find that S_bulk is constant in this model, eliminating the so-called initial entropy problem simply and elegantly. The GSL may still be relevant, however, principally in selecting the arrow of time, given that S_h ~ t^2 in this model., Comment: 12 pages, 1 Table. Accepted for publication in EPJ-C

We present a learning-based approach for virtual try-on applications based on a fully convolutional graph neural network. In contrast to existing data-driven models, which are trained for a specific garment or mesh topology, our fully convolutional model can cope with a large family of garments, represented as parametric predefined 2D panels with arbitrary mesh topology, including long dresses, shirts, and tight tops. Under the hood, our novel geometric deep learning approach learns to drape 3D garments by decoupling the three different sources of deformations that condition the fit of clothing: garment type, target body shape, and material. Specifically, we first learn a regressor that predicts the 3D drape of the input parametric garment when worn by a mean body shape. Then, after a mesh topology optimization step where we generate a sufficient level of detail for the input garment type, we further deform the mesh to reproduce deformations caused by the target body shape. Finally, we predict fine-scale details such as wrinkles that depend mostly on the garment material. We qualitatively and quantitatively demonstrate that our fully convolutional approach outperforms existing methods in terms of generalization capabilities and memory requirements, and therefore it opens the door to more general learning-based models for virtual try-on applications.