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This paper presents an adaptive shunted electrodynamic metamaterial, for broadband robust vibration control. The study considers a unit cell of 12 miniature, low-cost proof-mass actuators for the control of vibration in a three degree-of-freedom structure subject to parametric uncertainty. In order to modify their dynamic responses, each actuator is connected to a shunt circuit consisting of a parallel resistor and a switched in/out inductor and capacitor. Provided the impedance of the actuator is cancelled out using a negative impedance, the shunt circuit is capable of tuning the resonance of the actuator up or down in frequency. An adaptive tuning approach is proposed, whereby the shunted actuator resonance frequencies are periodically switched to the centre frequencies of the highest magnitude bins of a real-time frequency analysis of the velocity measured on the structure. This approach is compared to a blind swept tuning method and a fixed-shunt tuning in terms of the robustness to parametric uncertainty, and in practical terms for realisation using analogue or digital shunt impedances. • An electrodynamic metamaterial is proposed. • The effect of a parallel RLC shunt is examined. • An adaptive tuning algorithm is proposed. • The adaptive approach is compared to a blind swept tuning approach. • The adaptive approach is shown to have greater robustness to structural uncertainties. [ABSTRACT FROM AUTHOR]

This manuscript presents an experimental investigation into the effectiveness of Sliding Mode Control (SMC) for vibration mitigation of a structure equipped with a Hybrid Mass Damper (HMD), as compared to conventional linear controllers. The experimental setup involves a cantilever beam exhibiting a first bending mode at 21Hz, subjected to strong parametric uncertainties and external excitation through a shaker. Additionally, an inertial actuator with internal resonance at 21Hz is utilized to apply passive and active control to the vibrating beam. The SMC approach is compared to three more common HMD control methods: α − controller, LQG, and passivity-based control. To highlight the robustness and performance advantages of SMC, the nominal system undergoes significant mass and inertia modifications, not accounted for during the tuning of the observer and the controllers. The performances of the various controllers are then compared on four scenarios: two nominal identified systems and two configurations with uncertain parameters. Experimental results underscore the superior capabilities of SMC in mitigating structural vibrations, showcasing not only enhanced stability but also superior performance when confronted with substantial parametric uncertainties, outperforming traditional linear control methods. • Nonlinear control methods improve active vibration mitigation efficiency. • 3 commonly used control methods are compared with SMC on the control of a HMD on a vibrating beam. • SMC is robust against significant shifts in controlled modal frequencies and maintains the performances. [ABSTRACT FROM AUTHOR]

With the widespread use of powerful image editing tools, image tampering becomes easy and realistic. Existing image forensic methods still face challenges of low generalization performance and robustness. In this letter, we propose an effective image tampering localization scheme based on ConvNeXt encoder and multi-scale Feature Fusion (ConvNeXtFF). Stacked ConvNeXt blocks are utilized as an encoder to capture hierarchical multi-scale features, which are then fused in decoder for locating tampered pixels accurately. Combined loss function and effective data augmentation strategies are adopted to further improve the model performance. Extensive experimental results show that both localization accuracy and robustness of the ConvNeXtFF scheme outperform other state-of-the-art ones. The source code is available at https://github.com/multimediaFor/ConvNeXtFF. [ABSTRACT FROM AUTHOR]

Tractor path following control usually suffers from the uncertainties and disturbances especially when tractors are faced with uneven soil. As the main actuator of path tracking control, a electro-hydraulic coupling system has parameter uncertainties and disturbances. Conventional methods pay little attention to the electro-hydraulic coupling system which can generate poor path tracking performance. A layered multi-loop robust control architecture (LMLRC) is proposed including a path tracking layer, an angle tracking layer, and a current tracking layer. The path tracking layer is obtained by model predictive controller (MPC) considering uncertainties and disturbances. The angle tracking layer has a sliding mode controller (SMC) which takes parameter uncertainties and disturbances of electro-hydraulic coupling system into consideration. It is designed to follow the target angle generated by path tracking layer and outputs a tracking torque. The current tracking layer is designed by Proportion-Integration-Differentiation (PID) controller to track the torque output by angle tracking layer. To verify the proposed controller, simulation test and hardware in the loop (HIL) test were implemented. The results show that when faced with uncertainties and disturbances the LMLRC is more effective at path following. Moreover, better dynamic stability is exhibited when compared with other controllers. • Electro-hydraulic coupling system describes steering characteristics of tractor. • Layered multi-loop robust control architecture for path following. • Electro-hydraulic coupling system hardware in the loop control scheme. • Path following control under different soil conditions and different paths. • Steering control system can resist uncertainties and disturbances. [ABSTRACT FROM AUTHOR]

Advances in synthetic biology have enabled robust control of cell behavior by using tunable genetic circuits to regulate gene expression in a ligand-dependent manner. Such circuits can be used to direct the differentiation of pluripotent stem cells (PSCs) towards desired cell types, but rational design of synthetic gene circuits in PSCs is challenging due to the variable intracellular environment. Here, we provide a framework for implementing synthetic gene switches in PSCs based on combinations of tunable transcriptional, structural, and posttranslational elements that can be engineered as required, using the vanillic acid-controlled transcriptional activator (VanA) as a model system. We further show that the VanA system can be multiplexed with the well-established reverse tetracycline-controlled transcriptional activator (rtTA) system to enable independent control of the expression of different transcription factors in human induced PSCs in order to enhance lineage specification towards early pancreatic progenitors. This work represents a first step towards standardizing the design and construction of synthetic gene switches for building robust gene-regulatory networks to guide stem cell differentiation towards a desired cell fate. • Rational design guidelines to implement synthetic gene switches in hPSCs. • Systematic characterization of tunable elements for the assembly of synthetic gene switches. • Gene dual-switch-guided differentiation protocol improves pancreatic progenitor specification. [ABSTRACT FROM AUTHOR]

The Volterra filter is proven to be a structurally simple method for nonlinear active noise control (NLANC). However, its wide application is deeply constrained by computational complexity. This paper proposes a tap-decomposed Volterra filter, in which a long-tap Volterra filter is approximated as multiple short-tap filters by the nearest Kronecker decomposition. Since the number of taps of decomposed filters is much less than that of the original counterpart, the proposed method greatly reduces the implementation cost of the Volterra filter. Moreover, for impulsive noise sources in NLANC, decomposed filters are updated using the maximum correntropy criterion derived from information theory. Simulation experiments with different noise source types highlight the noise control performance of the proposed approach. [ABSTRACT FROM AUTHOR]

[Display omitted] • Precision MIMO vibration control with model uncertainties. • Comprehensive position/force model contains displacement, load, and force characteristics. • Robust MIMO control strategy with additive perturbation and mixed sensitivity. • Various experiments and validations. This paper presents dynamic modeling and robust vibration control for a high-speed macro–micro gripping system comprised of an air-floating macro-positioning stage and a compliant piezoelectric microgripper. A dynamic displacement coupling model is established considering rigid-flexible coupled macro-micromechanical structures, electromechanical properties containing the piezoelectric actuator and power amplifier, and hysteresis nonlinearity. Then, a comprehensive position/force model is proposed by integrating displacement, load, and force characteristics to describe the position and gripping force of micro-objects. Regarding system disturbances and model uncertainties, robust control strategies for pure displacement and position/force cooperation are devised utilizing perturbation H ∞ controllers and Kalman filters. Experimental results indicate that measurement noises for pure displacement and position/force are effectively reduced. Position/force disturbances arising from sudden mutual changes are decreased from 14.39 μm and 7.25 mN to 0.82 μm and 0.85 mN. Also, position/force vibrations excited by high-speed macro motion are suppressed by 31.99 % and 58.06 %. Both precision tracking and vibration suppression are still well achieved, even at significant parameter perturbation. Thus, experiments verify the feasibility of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Rotating shaft instability leading to high amplitude vibration is a key problem with a wide range of industrial applications. The purpose of this study is to identify the response statistics for a conceptual model of a flexible shaft supported on hydrodynamic bearings (HDBs) and propose design strategies to reduce the response. A conceptual model of a flexible shaft with three discs supported on the HDBs is developed. A linearised model of the system is developed using the short bearing assumption. A Campbell diagram is developed for the conceptual model to identify the critical speeds wherein a high response is produced. These high responses occur at the rotor speeds corresponding to the synchronous forward whirl and the oil whip. The identified rotor speeds are validated by carrying out a run-up analysis on the test rig. Since the manufacturing process and operating conditions induce uncertainty by varying the eccentricity and the relative phase of the unbalance mass, the influence of the whirling modes on the response is investigated. The nominal positions of the unbalance masses are such that the predominant mode excited is the first mode. Experimentally a scatter plot on the maximum response at the bearing location is identified using six different positions of the eccentric mass. Further, a stochastic analysis using reduced order nonlinear model of the system is carried out. The probability density obtained is compared with a Monte Carlo simulation and the bounds of the experimental results. A control strategy is designed by altering the relative position of the unbalance masses to reduce the excitation of the first mode. The robustness of the strategy is verified by using a stochastic analysis, which indicates a reduction in the response magnitude. • The dynamic behaviour of a flexible rotating shaft is investigated using a test rig. • High response Rotor speeds due to forward whirl and oil whip are identified • Maximum response statistics is determined using gPC based Stochastic analysis. • Results obtained from gPC and MC simulations are compared with experimental results. • A robust control strategy to reduce the vibration is proposed and tested by using gPC. [ABSTRACT FROM AUTHOR]

This work presents a new computational optimization framework for the robust control of parks of Wave Energy Converters (WEC) in irregular waves. The power of WEC parks is maximized with respect to the individual control damping and stiffness coefficients of each device. The results are robust with respect to the incident wave direction, which is treated as a random variable. Hydrodynamic properties are computed using the linear potential model, and the dynamics of the system is computed in the frequency domain. A slamming constraint is enforced to ensure that the results are physically realistic. We show that the stochastic optimization problem is well posed. Two optimization approaches for dealing with stochasticity are then considered: stochastic approximation and sample average approximation. The outcomes of the above mentioned methods in terms of accuracy and computational time are presented. The results of the optimization for complex and realistic array configurations of possible engineering interest are then discussed. Results of extensive numerical experiments demonstrate the efficiency of the proposed computational framework. • The control optimization of wave energy converters arrays is treated as a stochastic optimization problem. • Stochastic approximation (SA) and sample average approximation (SAA) methods are compared. • Indications for the preliminary control design are obtained. [ABSTRACT FROM AUTHOR]

Structural engineering developments have implemented new design techniques that allow the construction of large structures with lighter and high-strength elements. Structures such as grandstands, slabs and pedestrian bridges may be prone to excessive vibrations due to human activities. Traditional dynamic modeling approaches to represent the human–structure interaction (HSI) are often used to predict the dynamic behavior of the coupled system based on deterministic mass–spring–damper (MSD) mechanical components. However, these models cannot precisely represent variabilities and uncertainties in human and structural systems. In this study, three HSI robust models are developed and experimentally validated on a test structure. First, a traditional MSD coupled model is proposed where the human subsystem includes structured uncertainties in its biomechanical parameters. Then, two models are developed and synthesized from robust control theory using H ∞ and μ -synthesis, implementing structured uncertainties in the frequency and damping ratio of the plant to capture the intra- and inter-variability in the biomechanical parameters. The results demonstrate that the developed robust HSI models provide effective and reliable approaches to predict the range of probable responses of the coupled human–structure system for different occupants. [ABSTRACT FROM AUTHOR]

Nitric oxide synthase (NOS) inhibition with N(G)-monomethyl- l -arginine (L-NMMA) is often used to assess the role of NO in human cardiovascular function. However, the window of effect for L-NMMA on human vascular function is unknown, which is critical for designing and interpreting human-based studies. This study utilized the passive leg movement (PLM) assessment of vascular function, which is predominantly NO-mediated, in 7 young male subjects under control conditions, immediately following intra-arterial L-NMMA infusion (0.24 mg⋅dl−1⋅min−1), and at 45–60 and 90–105 min post L-NMMA infusion. The leg blood flow (LBF) and leg vascular conductance (LVC) responses to PLM, measured with Doppler ultrasound and expressed as the change from baseline to peak (ΔLBF peak and ΔLVC peak) and area under the curve (LBF AUC and LVC ACU), were assessed. PLM-induced robust control ΔLBF peak (1135 ± 324 ml⋅min−1) and ΔLVC peak (10.7 ± 3.6 ml⋅min−1⋅mmHg−1) responses that were significantly attenuated (704 ± 196 ml⋅min−1 and 6.7 ± 2 ml⋅min−1⋅mmHg−1) immediately following L-NMMA infusion. Likewise, control condition PLM ΔLBF AUC (455 ± 202 ml) and ΔLVC AUC (4.0 ± 1.4 ml⋅mmHg−1) were significantly attenuated (141 ± 130 ml and 1.3 ± 1.2 ml⋅mmHg−1) immediately following L-NMMA infusion. However, by 45–60 min post L-NMMA infusion all PLM variables were not significantly different from control, and this was still the case at 90–105 min post L-NMMA infusion. These findings reveal that the potent reduction in NO bioavailability afforded by NOS inhibition with L-NMMA has a window of effect of less than 45–60 min in the human vasculature. These data are particularly important for the commonly employed approach of pharmacologically inhibiting NOS with L-NMMA in the human vasculature. • The nitric oxide synthase (NOS) inhibitor N(G)-monomethyl-L-arginine (L-NMMA) is presumed to have long-lasting effects.ITh • The current data demonstrate that the window of effect for L-NMMA inhibition in the human vasculature is <45–60 min. • Experiments using intra-arterial L-NMMA infusion in humans can be strengthened by a more flexible infusion order. [ABSTRACT FROM AUTHOR]

• Proposed an unsupervised segment-based method for robust voice activity detection. • Proposed modified rVAD that uses computationally fast spectral flatness calculation. • Evaluated rVAD in terms of VAD performance using RATS and Aurora-2 databases. • Evaluated rVAD in terms of speaker verification performance using RedDots 2016. • rVAD showed favorable performance on various difficult tasks over existing methods. This paper presents an unsupervised segment-based method for robust voice activity detection (rVAD). The method consists of two passes of denoising followed by a voice activity detection (VAD) stage. In the first pass, high-energy segments in a speech signal are detected by using a posteriori signal-to-noise ratio (SNR) weighted energy difference and if no pitch is detected within a segment, the segment is considered as a high-energy noise segment and set to zero. In the second pass, the speech signal is denoised by a speech enhancement method, for which several methods are explored. Next, neighbouring frames with pitch are grouped together to form pitch segments, and based on speech statistics, the pitch segments are further extended from both ends in order to include both voiced and unvoiced sounds and likely non-speech parts as well. In the end, a posteriori SNR weighted energy difference is applied to the extended pitch segments of the denoised speech signal for detecting voice activity. We evaluate the VAD performance of the proposed method using two databases, RATS and Aurora-2, which contain a large variety of noise conditions. The rVAD method is further evaluated, in terms of speaker verification performance, on the RedDots 2016 challenge database and its noise-corrupted versions. Experiment results show that rVAD is compared favourably with a number of existing methods. In addition, we present a modified version of rVAD where computationally intensive pitch extraction is replaced by computationally efficient spectral flatness calculation. The modified version significantly reduces the computational complexity at the cost of moderately inferior VAD performance, which is an advantage when processing a large amount of data and running on low resource devices. The source code of rVAD is made publicly available. [ABSTRACT FROM AUTHOR]

• This work presents an analysis of a DNN-based autoencoder for speech enhancement, dereverberation and denoising for robust speaker verification (SV). • It includes a detailed performance analysis for different SV system paradigms (i-vectors vs. x-vectors), features and test conditions. • Presented results are computed on the public and widely used NIST SRE 2010, 2016, PRISM and SITW. • It also presents results achieved with pure autoencoder enhancement, multi-condition PLDA training and their simultaneous use. • Best system performance is achieved by combining the DNN autoencoder with x-vectors and multi-condition training in PLDA. In this work, we present an analysis of a DNN-based autoencoder for speech enhancement, dereverberation and denoising. The target application is a robust speaker verification (SV) system. We start our approach by carefully designing a data augmentation process to cover a wide range of acoustic conditions and to obtain rich training data for various components of our SV system. We augment several well-known databases used in SV with artificially noised and reverberated data and we use them to train a denoising autoencoder (mapping noisy and reverberated speech to its clean version) as well as an x-vector extractor which is currently considered as state-of-the-art in SV. Later, we use the autoencoder as a preprocessing step for a text-independent SV system. We compare results achieved with autoencoder enhancement, multi-condition PLDA training and their simultaneous use. We present a detailed analysis with various conditions of NIST SRE 2010, 2016, PRISM and with re-transmitted data. We conclude that the proposed preprocessing can significantly improve both i-vector and x-vector baselines and that this technique can be used to build a robust SV system for various target domains. [ABSTRACT FROM AUTHOR]

• A novel robust output feedback control method is proposed in this paper to deal with the nonlinearities, uncertainties and modeling errors. • A high-gain observer based on output feedback is designed to estimate the unmeasured states. • The close-loop stability of proposed controller is proofed. • The effectiveness of proposed method is verified on a hybrid electric powertrain bench. Precise and stable position control is critical in automatic clutch system, which is a challenging issue because of its nonlinear characteristics, uncertainties of parameters and disturbances, and lack of all-state sensors. Therefore, a novel robust output feedback control method is proposed in this paper to deal with the nonlinearities, uncertainties and modeling errors. First, an electromechanical dry clutch actuator system is formulated by theoretical analysis combined with experimental data. Then, a robust controller is designed, including the recursive control law calculation and a high-gain observer based on output feedback to estimate the unmeasured states. Next, simulations are carried out to verify the effectiveness of proposed method. Finally, experiments are conducted on a hybrid electric powertrain bench and the results show that the proposed method has a satisfactory performance. [ABSTRACT FROM AUTHOR]

• A novel actuator dynamics compensation method is proposed for RTHS. • H ∞ controller, polynomial extrapolation and adaptive filter are included. • The compensator is robust considering uncertainties and varying time delay in RTHS. • Effectiveness is validated through virtual RTHSs on the Benchmark model. Real-time hybrid simulation (RTHS) is a practical, cost-effective, and versatile experimental technique to evaluate structural performance under dynamic excitation. The simulated structure is commonly split into a physically tested rate-dependent substructure (PS) and a numerically simulated substructure (NS). A transfer system such as a servo-hydraulic actuator is used to impose boundary conditions on the PS. Consequently, efficient actuator control is necessary to guarantee reliable simulation results. However, time delay and uncertainties exist due to the dynamics of the actuator, which adversely influence the accuracy and stability of RTHS. Therefore, an innovative robust actuator dynamics compensation method is proposed in this study comprising three components, namely a mixed sensitivity-based robust H ∞ controller to stabilize the actuator–specimen dynamics, a polynomial extrapolation module to further cancel the actuator delay, and an adaptive filter for displacement reconstruction of the actuator–specimen system. A detailed design procedure of the proposed strategy is presented. The efficacy of the proposed strategy is validated through a series of virtual tests on the benchmark problem for RTHS. Results show that the proposed method exhibits excellent tracking performance and robustness. In particular, the maximum values of the calculated time delay (TD), root mean square of the tracking error (RMSE), and peak tracking error (PE) are 0 ms, 2.56%, and 2.12%, respectively, whereas the maximum values of the standard deviation of TD, RMSE, and PE are 0 ms, 0.25%, and 0.33%, respectively. [ABSTRACT FROM AUTHOR]

• Disturbance attenuation of flexible shaft in variable rotational speeds. • Development of a repetitive disturbance observer-based controller. • Experimental performance evaluation of the developed algorithm on a multi-input-multi-output active magnetic bearing system. Active magnetic bearings (AMB)s are attractive replacement of hydrostatic bearings mainly due to the absence of mechanical frictions, high-precision and low maintenance costs in high-speed and high-precision applications. However, they are inherently open-loop unstable and hence feedback controllers are essential for stabilisation of AMB equipped systems. This paper studies the robust stabilization and harmonic disturbance rejection of a flexible shaft and an AMB system. Firstly, a H ∞ loop-shaping method is utilized to stabilize the flexible shaft while the system is stationary. Then, an inner-loop is introduced to the feedback loop to minimize vibration performance measures while the shaft is in rotation at various rotational speeds. The inner-loop aims to combine the features of the common disturbance observer-based controllers and the repetitive controllers. The performance of the developed algorithm is experimentally verified on a multi-input-multi-output active magnetic bearing system. The experimental results demonstrate that the combination of the feedback H ∞ - controller and the inner-loop repetitive disturbance observer-based controller (RDOBC) not only robustly stabilizes the AMB system and greatly reduces the gain amplification of harmonic disturbances at fundamental frequencies, but it also reduces the gain amplification of the non-repetitive frequencies substantially. [ABSTRACT FROM AUTHOR]

• Ballbot dynamics divided into two sub-dynamics are modeled as nonlinear differential equations. • A controller combining PFL and SMC is proposed to balance and transfer of the ballbot. • The system state quickly reaches the target sliding mode surface using the proposed control. • The combined control has better control performance than other controllers and has solid robustness. A ridable ballbot, a personal robot, is an underactuated system. The four state variables of the ball motion and body motion are controlled by two input signals acting on the ball. Euler–Lagrange equation is applied to obtain the dynamic model of the ridable ballbot. On the basis of this dynamic model, a nonlinear controller is analyzed and designed to control balancing and transferring of the robot. The nonlinear control scheme is proposed based on the combination of two control design techniques: (i) partial feedback linearization, which is designed to maintain the body in the upright position; and (ii) sliding mode control, which provides robust control in ball motion on the floor against model imprecision, uncertainty of system parameters and friction, and external disturbances. These two control mechanisms are successfully merged into a combined controller. Numerical and experimental results indicate the effectiveness of the combined controller and proposed a dynamic model. The control algorithm asymptotically stabilizes all system responses. [ABSTRACT FROM AUTHOR]

• Dynamic formulation of a deployable cable-driven robot considering the actuator models was derived. • A novel control topology based on a cascade structure was proposed. • Stability of the system as a whole was analyzed via Lyapunov direct method. • The novel method was implemented on a deployable cable-driven robot called ARASCAM. • Results showed that our controller achieved a suitable tracking performance. In this paper, we derive the dynamic formulation of a deployable cable-driven robot that considers models of the actuator and power transmission systems, and we investigate the challenges of structural uncertainty. To accommodate the inherent uncertainty of the system, we propose a proper control topology based on a cascade structure. The inner loop of the structure controls the cable forces, and the outer loop tracks the precise position of the robot's end-effector. For the design of the outer loop controller, we propose a robust sliding mode controller with a stability analysis that is based on the Lyapunov direct method. The main contribution of this paper is to analyze the stability of the system as a whole considering both the inner and outer loop controllers. Finally, in order to illustrate the performance of the proposed controller, we present the results of an experiment on a deployable suspended cable-driven robot, which shows the effectiveness of the proposed controller in the presence of the inherent uncertainties of the system. [ABSTRACT FROM AUTHOR]

In order to maximize the advantage of four-wheel independently actuated (FWIA) technology, the paper proposes a new configuration and dynamics control method. It is called relaxed static stability (RSS). First, RSS allows lateral dynamics system of the vehicle to be inherently unstable to enhance overall structure design flexibility. Three FWIA electric vehicles (a racecar, a passenger car, and a military vehicle) are shown to demonstrate the benefit of RSS. By applying the RSS, the configuration flexibilities of the battery pack location of these vehicles are improved by 95.4%, 167% and 20%, respectively. Second, the closed-looped handling stability could be improved by yaw moment control through the pole assignment technique. The target pole's positions of closed-looped lateral dynamics system are selected according to various performance requirements. Four cases of target pole locations are given as examples to show the principle of selecting the target pole locations for different type vehicles. A pole assignment controller is designed with the consideration of the parametric uncertainties caused by tire nonlinear behavior. Finally, an FWIA electric vehicle, which is built by the authors, is used as testbed to show the benefit of the RSS. The experiments in step steer and sine steer conditions prove the efficiency and robustness of the controller, as well as the convenience of adjusting target pole locations according to various performance requirements. [ABSTRACT FROM AUTHOR]

• We use context fusion and subspace constraint to improve correlation filter tracking. • A model updating strategy for accelerating the tracker is proposed. • Ablation study on each component of the proposed method is analyzed in detail. • Extensive experiments verify the effectiveness of the proposed tracker. In the trackers based on correlation filter, the limitation of tracking samples and the efficiency of the solution affect the performance of the trackers. Based on the Discriminative Scale Space Tracking(DSST), we proposed a new framework to improve correlation filter tracking via context fusion and subspace constraint. First, the target and background are jointly modeled to enhance the ability to distinguish between target and background. Second, to achieve higher robustness, subspace constraint is applied to DSST, and it can be solved efficiently by subspace-based alternating direction method of multipliers(SADMM). Finally, through the model update strategy, high-quality samples are selected to update. The proposed algorithm is validated on the OTB2013, OTB2015 and TC128. Experimental results show that compared with fast DSST(fDSST), success rate and accuracy of the proposed method are increased by 4.2% and 6.8% respectively. Compared with the state-of-the-art trackers, the proposed method achieves a competitive result with real-time speed. [ABSTRACT FROM AUTHOR]

Improving the ability of imperceptibility, watermark capacity, and robustness at the same time still remains a challenge within the digital image watermarking community. By modeling the robust nonsubsampled Contourlet transform (NSCT) difference coefficients with vector based Cauchy distribution and employing locally most powerful (LMP) test, we propose a locally optimum image watermark decoder in NSCT domain. We first compute the difference coefficients according to the inter-scale dependency between NSCT coefficients, and investigate the robustness of the NSCT difference coefficients by subjective visual error and objective mean squared error (MSE) terms. We then embed the digital watermark into the significant NSCT difference subband with highest energy by modifying the robust NSCT difference coefficients. At the receiver, by combining the vector based Cauchy probability distribution and LMP test, we propose a locally optimum blind watermark decoder in the NSCT domain. Here, robust NSCT difference coefficients are firstly modeled by employing the vector based Cauchy probability density function (PDF), where the Cauchy marginal statistics and various strong dependencies of NSCT coefficients are incorporated. Then the statistical model parameters of vector based Cauchy PDF are estimated using second-kind statistics approach. And finally a blind image watermark decoder is developed using vector based Cauchy PDF and LMP decision rule. We conduct extensive experiments to evaluate the performance of the proposed blind watermark decoder, in which encouraging results validate the effectiveness of the proposed technique, in comparison with the state-of-the-art approaches recently proposed in the literature. [ABSTRACT FROM AUTHOR]

We present a regression technique for data-driven problems based on polynomial chaos expansion (PCE). PCE is a popular technique in the field of uncertainty quantification (UQ), where it is typically used to replace a runnable but expensive computational model subject to random inputs with an inexpensive-to-evaluate polynomial function. The metamodel obtained enables a reliable estimation of the statistics of the output, provided that a suitable probabilistic model of the input is available. Machine learning (ML) regression is a research field that focuses on providing purely data-driven input-output maps, with the focus on pointwise prediction accuracy. We show that a PCE metamodel purely trained on data can yield pointwise predictions whose accuracy is comparable to that of other ML regression models, such as neural networks and support vector machines. The comparisons are performed on benchmark datasets available from the literature. The methodology also enables the quantification of the output uncertainties, and is robust to noise. Furthermore, it enjoys additional desirable properties, such as good performance for small training sets and simplicity of construction, with only little parameter tuning required. • Data-driven polynomial chaos expansion (PCE) is an effective regression algorithm. • PCE yields both accurate point predictions and estimates of the output statistics. • Point prediction accuracy similar to neural networks and SVMs in considered real data. • Sparse PCE is applicable to high-dimensional feature spaces and small sample sets. • Simple to build, requires little parameter tuning, easy to interpret and portable. [ABSTRACT FROM AUTHOR]

Highlights • General normalized sparse filtering (GNSF) is proposed for intelligent fault diagnosis. • The value fetching principle of normalization parameters of the l (r − p / q) -norm is analyzed. • GNSP gives a wide suitable optional range for normalization parameters to guarantee the robustness of the algorithm. Abstract In the era of data deluge, "big data" generated by mechanical equipment creates higher requirements for the field of mechanical fault diagnosis. Intelligent diagnosis methods have become a new focus for researchers. Deep learning-based intelligent diagnosis method for health monitoring is a promising tool that can get rid of the dependence of prior signal processing knowledge and diagnostic experience. In this paper, a novel unsupervised learning method called general normalized sparse filtering (GNSF) is proposed for intelligent fault diagnosis. The proposed algorithm realizes the feature sparsity measurement by optimizing the objective function based on the generalized l r - p / q norm of the feature matrix. The characteristic of the generalized l r - p / q norm as sparsity measure is discussed. Based on GNSF, a new intelligent fault diagnosis method is designed and applied on rolling bearing and planetary gear fault diagnosis under complex working conditions. The verification results confirm that the proposed method is a promising tool that can obtain higher diagnosis efficiency and accuracy with fewer training samples and has much better robustness than the previous methods. [ABSTRACT FROM AUTHOR]

Highlights • A novel hybrid optimal control combining many different controls. • A validation through both simulation and experimental realization of vibration control. • A comparative study of the proposed control and existing hybrid controls. Abstract This study presents a new hybrid optimal controller to enhance the robustness and stability of dynamic systems subjected to uncertainties and external disturbances. In the formulation of the proposed control system, the Bolza–Meyer (BM in short) criterion of the optimal controller is modified to meet the system variation, and the sliding-mode controller is modified to obtain the classical control property with the prescribed performance. The proposed controller also includes the H-infinity technique as a bridge for connecting the sub-controls and improving the robust performance of the system. Fuzzy model are used as filters to choose the optimal values for the next calculation. Hence, many advantages of fuzzy model are acquired, related to optimal control, sliding mode control, prescribed performance, and H-infinity techniques. To demonstrate these advantages, the proposed hybrid controller is applied to a vehicle seat suspension for vibration control. Superior control performance over existing hybrid controllers is demonstrated in both time and frequency domains. [ABSTRACT FROM AUTHOR]

Abstract The traditional collaborative filtering (CF) suffers from two key challenges, namely, the normal assumption that it is not robust, and it is difficult to set in advance the penalty terms of the latent features. We therefore propose a hierarchical Bayesian model-based CF and the related inference algorithm. Specifically, we impose a Gaussian-Gamma prior on the ratings, and the latent features. We show the model is more robust, and the penalty terms can be adapted automatically in the inference. We use Gibbs sampler for the inference and provide a statistical explanation. We verify the performance using both synthetic and real datasets. Highlights • The Gamma-Gaussian assumption on the ratings. It is a heavy tail distribution so the model is more robust. • The Gamma-Gaussian assumption on the latent features. Hence we do not need to specify the regularization term manually. • The Gibbs sampling of the parameters and the statistical explanation of the updating formulas. [ABSTRACT FROM AUTHOR]

Highlights • A magnetic negative stiffness isolator based on Maxwell normal stress is proposed. • A magnetic spring is formed by the Maxwell normal stress to generate negative stiffness. • Active control is integrated into the isolator to realize the active-passive hybrid vibration isolation. • Prototype design and experimental results demonstrate the theoretical analysis. Abstract A new magnetic negative stiffness isolator based on Maxwell normal stress is proposed to improve the low-frequency vibration isolation performance of a system with high static support stiffness. The isolator can provide high negative stiffness maintaining compact size and light weight. The negative stiffness characteristic of the isolator was first modeled through magnetic circuit analysis. Then, the passive vibration isolation performance of the isolator was validated experimentally. Active control was then introduced into the isolation system to improve its ability to adapt to external disturbances and suppress the system's resonance response. The governing equation of the active-passive hybrid vibration isolation system was established. The simulated and experimental results showed that the magnetic negative stiffness isolator with active-passive hybrid control can greatly reduce the isolation initial frequency and significantly suppress the system resonance response in comparison with passive methods. The present work provides a promising way to realize low-frequency broad-band vibration isolation for systems with high static support stiffness. [ABSTRACT FROM AUTHOR]

Highlights • The method selects the best onset time obtained from different competitive techniques. • Onset times are important features of AE signals to identify source location. • It is not feasible to manually identify AE signals from the noise. • The method can be easily programmed and used in an autonomous way. • The proposed method is extendable to other fields where competing data are available. Abstract Robust identification of the most accurate observed input data among a pool of observations is key in modeling and decision making. A statistically biased observed measurement deteriorates the predictive power of a model and affects decision-making ability based on the prediction of the model. When two competing methods of measurement are available, such as methods which identify arrival times in acoustic emission (AE) signals, a principal question is whether one of the two obtained datasets, or a combination of the two, should be used later on, for example, to localize an AE source. This question becomes more important when collecting not repeatable data such as AE signals created by a propagating crack. This paper considers an inverse source location problem in a concrete block to address the mentioned issue, a proposed methodology which also has wider application in competitive data selection. Elastic energy released by an AE event, such as a propagating crack, is recorded by acoustic emission data acquisition system. The onset time of AE signals is often used to locate the source of the event, and its accuracy directly affects the precision of source identification. This research proposes an innovative approach to select the most probable onset time obtained from two automatic picker methods. The proposed method selects the most probable onset times, which are observed by each picker for each sensor, in a probabilistic fashion. To validate the proposed method, the most accurate onset time observed by each picker is identified by visual inspection and is compared with the one is selected by the proposed method. Finally, the dataset is used for source location identification. Results show that picked onset times determined by the proposed method generate more accurate source identification when compared with coordinates obtained using each dataset individually. [ABSTRACT FROM AUTHOR]

Highlights • A GVM is used to characterize an unknown and complex structural component. • Damage detection based on GVMI approach is developed. • The key parameters on detection resolution are investigated. • Results in honeycomb sandwich structure show the validity of damage detection without any blind area. Abstract This paper presents a novel vibration-based damage detection method using a general vibration model identification (GVMI) approach. A damage index based on the identified general vibration model is constructed for damage detection and localization, with the damage being regarded as a virtual excitation on the structure. The proposed GVMI approach utilizes a general form of high order derivative equation that to be identified and further used to detect changes in the vibration characteristics of the structure. Therefore, the proposed damage detection method requires neither baseline signals nor prior knowledge on the structural parameters, thus offering great application potentials for complex structures with unknown parameters. As a proof-of-concept example, a honeycomb sandwich cantilever beam is investigated for validating the proposed approach. The influences of the key parameters on the detection resolution, such as the measurement interval, the order of the displacement derivative and the selection of the excitation frequency, are investigated. Furthermore, an enhanced version of the GVMI method with an excitation frequency extension is developed by using a data fusion scheme. Taking advantages of the broadband excitations, the blind inspection area can be completely eliminated, whilst improving the effectiveness and the accuracy of the detection. Experimental results with both single and multiple structural damage show the validity and the accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a nested adaptive super-twisting sliding mode (NASTSM) control scheme for a vehicle Steer-by-Wire (SbW) system. Firstly, the plant model of the SbW system is expressed as a second-order differential equation from the steering motor input voltage to the front wheel steering angle. Specifically, the model of the self-aligning torque is elaborated in detail and compared with a simplified one. Next, an NASTSM controller is designed for the SbW system, which adopts a nested adaptive law to promote tracking accuracy by dealing with complex time-varying external disturbances and a super-twisting sliding mode (STSM) control component to guarantee strong robustness while alleviating chattering phenomenon. The stability of the NASTSM control system is verified in the sense of Lyapunov. Finally, experiments are carried out under various conditions. The experimental results show that the proposed NASTSM controller owns superiority in terms of not only higher tracking precision and stronger robustness, but also less dependence on the information of plant models compared with a conventional adaptive sliding mode (CASM) controller. [ABSTRACT FROM AUTHOR]

Highlights • Detailed study on robustness of adaptive impact absorbing systems is presented. • Novel control system design for high-performance impact absorption is proposed. • Automatic path-planning approach eliminates requirement of impact identification. • Novel Hybrid Path Tracking provides robust realization of the optimal system path. • Superiority of the proposed system over classical adaptive absorbers is proved. Abstract The paper presents development, evaluation and comparison of various control systems for adaptive fluid-based absorbers serving for absorption of the impact loading. The investigations concern two competitive approaches: i) standard control systems with single determination of the optimal system path based on identified impact conditions, and ii) newly-developed control systems with on-line determination and update of the system path during the process. It is revealed that low robustness of the standard control systems to imprecise impact identification and unknown disturbances results from the assumed path-determination approach and utilized simple path-tracking methods. The proposed solution to this problem is application of the innovative control systems, which utilize Automatic Path Finding and Automatic Path Update algorithms based on full kinematic feedback as well as Hybrid Path Tracking method dedicated for fluid-based absorbers. The introduced approach to absorber control is used to develop three different self-adaptive systems of increasing complexity and robustness. The favourable capabilities of proposed systems including no need for impact identification, high robustness against force disturbances and reduction of leakages influence are proved. Detailed discussion is presented using the illustrative example of single-chamber adaptive pneumatic shock-absorber mitigating impact loading. [ABSTRACT FROM AUTHOR]

Highlights • Considering of the crane structural dynamics in the trolley motion control system. • Robust control-based approach for active damping of the gantry crane structural vibrations via trolley motion. • Approach for a modeling of the elastic gantry cranes with uncertain parameters and evaluation of robustness margins. • Simulations and experimental verifications of proposed damping approach. Abstract One of the most significant structural dynamics problems of large gantry cranes are elastic vibrations in trolley travel direction. They put additional mechanical stresses on a crane construction and reduce crane operation performance. As these vibrations are mostly excited by trolley acceleration forces, they can be taken into account in the trolley motion control system. This article presents a robust control-based approach for active damping of the gantry crane elastic structural vibrations. The crane model with additional uncertainty models are derived using the finite element method (FEM). In order to achieve low order models that can be directly used for the control system design modal truncation as a model reduction technique is performed. The robust controller is then designed by the H ∞ -loopshaping design procedure evaluating the associated robustness margins. The proposed feedback strategy has been successfully verified on a laboratory gantry crane. [ABSTRACT FROM AUTHOR]

Highlights • 2D modal Teager-Kaiser energy can reveal singularity of mode shapes. • Noise robustness is enhanced in 2D multi-resolution modal Teager-Kaiser energy. • A noise-robust damage indicator is formulated for damage identification. • The damage indicator is experimentally validated by non-contact laser measurement. Abstract Identification of delaminations in the early stage is crucial to ensure the safety of CFRP laminates. In the modal domain, high spatial sampling resolution of a mode shape is required for precise location of incipient delamination. Non-contact measurement relying on laser scanning technology allows measured mode shapes to be densely sampled. Unfortunately, noise components usually contaminate such densely-sampled mode shapes, easily causing intense noise interference that likely masks actual delamination features. To address this issue, a noise-robust damage indicator is formulated for designating the presence and location of incipient delamination in CFRP laminates under noisy environments. This damage indicator is created by introducing a 2D multi-resolution modal Teager-Kaiser energy for characterizing delamination-caused singularity of mode shapes. Proof of concept of the damage indicator is numerically undertaken in finite-element CFRP laminates with incipient delaminations, with emphasis on sensitivity to damage as well as robustness against noise. The applicability of the damage indicator is experimentally validated on a CFRP laminate with an incipient delamination, mode shapes of which are precisely acquired via the non-contact measurement using a scanning laser vibrometer. The numerical and experimental results show that the damage indicator can effectively identify the presence and location of incipient delamination in CFRP laminates under noisy environments. [ABSTRACT FROM AUTHOR]

Highlights • The dynamic equations are established and simplified for robust controller design. • The perturbation modeling of modal and milling parameters are built and analyzed. • The robust active controller is designed for milling chatter suppression. • The effectiveness and robustness of the designed controller are verified. Abstract Milling, as the most widely used processing method, plays an important role in advanced manufacturing. However, milling chatter has severely restricted the development of advanced manufacturing industry. Therefore, it is of great theoretical significance and application value to study the active control of milling chatter. This paper focuses on the design of robust controller for chatter suppression. At first, the milling dynamic equations with active control force are established and simplified with the approximate linear time-invariant model for robust controller design. Then, the perturbation modeling of modal parameters and milling parameters are built and analyzed. Based on the perturbation models, the control algorithm is designed. The numerical simulation in milling process verifies the effectiveness of the designed controller. In order to validate the practical effect, the flank milling and end milling tests are implemented, respectively. The experimental results show that the designed algorithm is able to suppress chatter greatly and lead to the better workpiece surface, which prove the effectiveness and robustness of the designed controller. [ABSTRACT FROM AUTHOR]

Highlights • Concurrent topology optimization for multiscale dynamic composite structures. • A hybrid dimensional reduction method to estimate interval mean and variance. • A new robust topology optimization method under random-interval hybrid uncertainties. • Robust objective function defined as a weighted sum of mean and standard variance. Abstract A new robust topology optimization method based on level sets is developed for the concurrent design of dynamic structures composed of uniform periodic microstructures subject to random and interval hybrid uncertainties. A Hybrid Dimensional Reduction (HDR) method is proposed to estimate the interval mean and the interval variance of the uncertain objective function based on a bivariate dimension reduction scheme. The robust objective function is defined as a weighted sum of the mean and standard variance of the dynamic compliance under the worst case. The sensitivity information of the robust objective function with respect to the macro and micro design variables can then be obtained after the uncertainty analysis. Several examples are used to validate the effectiveness of the proposed robust topology optimization method. [ABSTRACT FROM AUTHOR]

Highlights • A new tool added to the MLMM modal parameter estimation method is introduced. • This new tool automatically rejects the nonphysical modes from the mode set. • The physicalness of a mode is judged by examining its mass sensitivity. • The new tool cleans the mode set before starting the validation phase. • With the new MLMM tool, more robust modal models are obtained. Abstract In this paper, a new feature added to the MLMM modal parameter estimation method (Maximum Likelihood estimation of a Modal Model) will be introduced. The MLMM method tackles some of the remaining challenges in modal analysis, e.g. modal analysis of highly-damped cases where a large amount of excitation locations is needed such as the modal analysis of a trimmed car body. The MLMM modal parameter estimator uses the Levenberg-Marquardt optimization scheme to directly fit the modal model to the measured FRFs. The big advantage of the MLMM method over the existing methods is its capability to consider during the estimation phase that the estimated modal model will fulfill some important modal properties (Reciprocity, realness of the mode shapes, negative mass sensitivity of the modes, etc) that are traditionally used to check the physicalness of the estimated modal model in the validation phase afterwards. Therefore, the validation phase is partially combined with the estimation phase in one step. This merging of the estimation and validation steps will help to reduce the time needed for the last two steps of the modal analysis process (i.e., the estimation step & the validation step) and to make the modal parameter estimation step more objective process rather than subjective one. The MLMM method fully integrates, within the estimated modal model, some important physical constraints, which are required for the intended applications, e. g. realness of the mode shape and FRFs reciprocity. More classical modal parameter estimation methods have rarely the possibility to fully integrate these constraints and the obtained modal parameters are typically altered in a subsequent step to satisfy the desired constraints. It is obvious that this may lead to sub-optimal results. The new feature added to the MLMM method and introduced in this article enables the automatic rejection of the modes that have dubious poles (e.g. mathematical pole that models noise effects) from the mode set. This new feature uses the mass sensitivity (MS) as a criterion to judge the physicalness of the mode. The applicability of MLMM with the new added feature to estimate an accurate constrained modal model will be demonstrated using simulation example and two challenging industrial applications. [ABSTRACT FROM AUTHOR]

Abstract An extension of the well-known delayed resonator concept is proposed with the objective to enhance its robustness in vibration suppression under guaranteeing stability of the overall set up. The novel resonator feedback is based on an acceleration measurements passing through a delay of polynomial distribution. The feedback design is formulated as an optimization problem over the parameter set formed by the polynomial coefficients and the gain of the delay free part. The first objective is to minimize the sensitivity of the resonator performance with respect to variations of the excitation frequency, i.e. to widen its frequency stop-band centered at the nominal excitation frequency. The second, equally significant objective is to guarantee that this does not lead to instability of the overall mechanical system coupled with the absorber and to ensure sufficient stability margin. The arising non-convex and non-smooth optimization problem is widely discussed and a penalty method is developed, where the unconstrained problem in every iteration step is solved using available software tools for optimization and spectral design of time delay systems. The proposed design technique is validated by simulations for two case study examples. [ABSTRACT FROM AUTHOR]

Highlights • Robust method for transforming multiply scattered wave data to Born data is proposed. • The method is based on data-driven model order reduction. • Reduced model construction is purely linear algebraic. • Method is uniformly applicable to acoustic, elastic and electromagnetic data. • Built-in regularization via the spectral truncation of the data. Abstract We introduce a novel algorithm for nonlinear processing of data gathered by an active array of sensors which probes a medium with pulses and measures the resulting waves. The algorithm is motivated by the application of array imaging. We describe it for a generic hyperbolic system that applies to acoustic, electromagnetic or elastic waves in a scattering medium modeled by an unknown coefficient called the reflectivity. The goal of imaging is to invert the nonlinear mapping from the reflectivity to the array data. Many existing imaging methodologies ignore the nonlinearity i.e., operate under the assumption that the Born (single scattering) approximation is accurate. This leads to image artifacts when multiple scattering is significant. Our algorithm seeks to transform the array data to those corresponding to the Born approximation, so it can be used as a pre-processing step for any linear inversion method. The nonlinear data transformation algorithm is based on a reduced order model defined by a proxy wave propagator operator that has four important properties. First, it is data driven, meaning that it is constructed from the data alone, and it requires only a rough estimate of the background velocity (kinematics). Second, it can be factorized in two operators that have an approximately affine dependence on the unknown reflectivity. This allows the computation of the Fréchet derivative of the reflectivity to the data mapping which gives the Born approximation. Third, the algorithm involves regularization which balances numerical stability and data fitting with accuracy of the order of the standard deviation of additive data noise. Fourth, the algebraic nature of the algorithm makes it applicable to scalar (acoustic) and vectorial (elastic, electromagnetic) wave data without any specific modifications. [ABSTRACT FROM AUTHOR]

Highlights • Different from previous studies that use a single label as the gold-standard to decide dialogue breakdown, we focused on inherent subjectivity when people regard dialogue breakdowns and variationality due to the design of conversational agents. • We propose methods for dialogue breakdown detection that first cluster annotators based on their label distributions, and then ensemble detectors trained by different sets of annotators. • Two kinds of neural networks are used as detectors. We assume that breakdowns occur locally (word and phrase levels) and/or globally (sentence and context levels). The convolutional neural network (CNN) is applied to detect former type of breakdowns, and long short-term memory (LSTM) detects the latter type of breakdowns. • Our best model achieves 63.6% F-score on breakdown detection, which is 5.6% higher than the baseline using the conditional random fields. • This is an extended version of our workshop paper submitted to the Dialogue Breakdown Detection Challange 3 (DBDC3). We newly conducted additional experiments that confirm effectiveness of clustering annotators based on their annotation distributions. In addition, detailed error analysis revealed characteristics of the CNN and LSTM on the dialogue breakdown detection task. Abstract Dialogue breakdown is a significant problem in conversational agents. Timely breakdown detection helps the agents quickly recover from mistakes, minimizing the impact on user experience. In this paper, we focus on two problems: variations in determining a response that breakdowns a conversation i.e. , subjectivity, and variations in breakdown types due to designs of conversational agents, i.e. , variationality. To address the subjectivity, which decreases the agreement rate among annotators, our methods detect a dialogue breakdown by ensembling detectors trained by different sets of annotators that are grouped using a clustering algorithm. To address the variationality, our methods apply two types of detector architectures to capture global and local breakdowns. The long short-term memory detector considers the global context and the convolutional neural networks detector is sensitive to the local characteristics. The ensemble of all detectors makes a final judgment. The results of the Japanese task in the Dialogue Breakdown Detection Challenge 3 (DBDC3) confirm that our approach significantly outperforms the baseline, which uses the conventional conditional random field. Detailed error analysis reveals that our encoders based on a convolutional neural network and a long short-term memory have different characteristics. It also confirms the effects of annotator clustering. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the finite-frequency H ∞ control problem of active suspensions is investigated. In view of the human body is much sensitive to the vertical vibrations between 4 and 8 Hz, a robust finite-frequency state-feedback H ∞ controller is introduced to suppress the sprung vibration in the concerned frequency range. In order to simultaneously control the vehicle heave, roll, and pitch motions, a full-car suspension model is adopted. Suspension parameter uncertainties, actuator saturation, and suspension deflection constraints are all considered in the model, and a robust controller is designed to handle the parameter uncertainties and achieve the prescribed H ∞ performance. Simulation with different road conditions and parameter uncertainties are performed and the results illustrate the effectiveness of the proposed controller. Highlights • A full-car model is adopted to control the heave, pitch and roll motions. • The 4–8 Hz range disturbance is attenuated by a finite-frequency controller. • The parameter variations are considered to guarantee the system robustness. [ABSTRACT FROM AUTHOR]

The control problem for an uncertain mechanical system is addressed. The mechanical system under consideration, which is to follow a class of prescribed constraints, contains (possibly fast) time-varying uncertainty. The uncertainty is bounded, but the bound is not necessarily known. A new class of robust control schemes is proposed, which is based on on-line adaptation of a design parameter. Two categories of adaptation laws, both are based on self-adjusting leakage, are proposed. The control guarantees uniform boundedness and uniform ultimate boundedness of a performance measure. Comparing with the previous control scheme, the importance of this new control is that it can compensate the uncertainty in a very effective way. It also avoids over compensation and renders modest control effort. For demonstration purpose, the Toda Lattice, an ideal model of mechanical system with asymmetrical behaviour is chosen. The energy control of the Toda Lattice demonstrates the validity and reliability of control schemes and adaptation laws. [ABSTRACT FROM AUTHOR]

In the design of the Attitude Control System (ACS) for a Rigid-flexible Satellite (RFS) it is important to take into account the influence of the structure’s flexibility, since its interaction with the satellite rigid motion during translational and/or rotational maneuver, can damage the ACS pointing accuracy. In the linearization and/or reduction of the RFS mathematic model, usually one loses some important information associated with the satellite true dynamical behavior. One way to recovery this information is to include in the ACS design the parametric and non parametric uncertainties of the system. The H infinity control methodology is able to take into account the parametric uncertainty in the control law design so as the controller becomes more robust. This paper presents the design of a robust controller using the H infinity control technique to control the attitude of a RFS which is represented by a flexible beam connected to a central rigid hub considering the set of parametric uncertainty. The results of the simulations have shown that the control law designed with the H-infinity control method was robust enough to support the action of uncertainties and control the RFS attitude and suppress its panel’s vibrations. [ABSTRACT FROM AUTHOR]

The problem of how to accurately reconstruct the multicomponent signal from the ridges of the short-time Fourier transform (STFT) is considered. Especially when time-frequency representations contain strong noise corruption and crossed components, exact reconstruction becomes more difficult. In this paper, we propose two robust ridge reconstruction approaches, i.e., weighted ridge reconstruction (WRR) and self-paced ridge reconstruction (SPRR). The former is aim to select a more robust loss function to eliminate the influence of the strong noise and outliers. A half-quadratic optimization algorithm is developed to solve the proposed problem efficiently. The latter incorporates self-paced learning (SPL) method into ridge reconstruction model to sequentially include ridge points into signal reconstruction from easy to complex, which not only can suppress noise and outliers, but also can avoid a bad result in the presence of missing observations. Simulation and real-life signals are employed to show the effectiveness and practicability of the proposed approaches. [ABSTRACT FROM AUTHOR]

Abstract Finite element (FE) model updating has emerged as a powerful technique for structural health monitoring and damage identification of civil structures. Updating mechanics-based nonlinear FE models allows for a complete and comprehensive damage diagnosis of large and complex structures, but it is computationally demanding. This paper first introduces an Iterated Extended Kalman filter (IEKF) to update mechanics-based nonlinear FE models of civil structures. Different model updating techniques using the Extended Kalman filter (EKF), Unscented Kalman Filter (UKF) and IEKF, are then compared for their performance in terms of convergence, accuracy, robustness, and computational demand. Finally, a non-recursive estimation procedure is presented and its effectiveness in reducing the computational cost, while maintaining accuracy and robustness, is demonstrated. An application example is presented based on numerically simulated response data for a three-dimensional 5-story 2-by-1 bay reinforced concrete (RC) frame building subjected to bi-directional earthquake excitation. Excellent estimation results are obtained with the EKF, UKF, and IEKF used in conjunction with the proposed non-recursive estimation approach. Because of the analytical linearization used in the EKF and IEKF, abrupt and large jumps in the estimates of the model parameters are observed with these filters, which may lead to divergence of the nonlinear FE model solution procedure. The UKF slightly outperforms the EKF and IEKF, but at a higher computational cost. [ABSTRACT FROM AUTHOR]

An offshore ship-mounted crane consisting of a trolley, a wire, and a payload, is a typical nonlinear underactuated system, which suffers from unmatched disturbances mainly caused by sea waves and currents. Besides, unknown or uncertain parameters may cause vertical positioning errors or make accurate gravity compensation impossible, which may induce various risks during the transportation process. In terms of the aforementioned problems, this paper studies the adaptive robust tracking control problem for an offshore ship-mounted crane. In particular, a new adaptive robust coupling control approach, with adaptive laws included to deal with unknown parameters, and robust terms included to handle unknown disturbances, especially unmatched disturbances, is constructed in this paper, which achieves simultaneous load swing suppression and disturbances elimination. Without any approximation to the original nonlinear model, it is rigorously proven that the proposed method can ensure the stability of the overall crane system’s equilibrium point, as supported by Lyapunov techniques. Finally, some contrast simulations and experimental results are collected to verify the superior performance of the proposed controller. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • NMR peaks can be fit in the real and imaginary domain. • The use of complex-valued data improves optimization robustness and precision. • The approach is implemented in an R language package "rnmrfit". Abstract Quantitative NMR is intrinsically dependent on precise, accurate, and robust peak area calculation. In this work, we demonstrate how the use of complex-valued peak descriptions can improve peak fitting in the frequency domain — incorporating phase and baseline correction as well as apodization while working with commonly used Fourier-transformed data. The method has been implemented in an open source R package called rnmrfit that is available for download on GitHub (https://github.com/ssokolen/rnmrfit). Application to real data suggests that this approach can also result in dramatically higher precision than can be achieved with existing software. Simulation data indicates that coefficients of variation below 0.1% can be readily achieved at signal to noise (SNR) ratios of approximately 100. The use of complex-valued data in the frequency domain is demonstrated as a relatively simple and effective means of improving peak fitting for quantitative NMR analysis. [ABSTRACT FROM AUTHOR]

Abstract The high-order flux reconstruction (FR) method on unstructured hexahedral grids is coupled with the localized artificial diffusivity (LAD) scheme for aiming at accurate simulation of shock-turbulence interaction. In order to overcome known robustness issues particularly with the high-order (r > 0) derivative formulation, important properties of the artificial bulk viscosity (ABV) profile affecting the solution are investigated first. For the purpose of comparison, an approximated Gaussian filter and modified restriction-prolongation (RP) filters are developed and tested for the present FR-LAD approach. Then, we propose a multidimensional RP filter on unstructured quadrilateral and hexahedral grids to address the issues on non-Cartesian grids. It is shown that a simple extension of a one-dimensional RP filter for two-dimensional grids may result in insufficient smoothing of ABV with r = 2 , and that the proposed multidimensional RP filter can provide smooth ABV with both r = 0 and r = 2. The proposed FR-LAD scheme is tested for typical shock-related problems, including the 1D shock tube, 1D shock-entropy wave interaction, 2D steady shock flows, and 2D shock-vortex interaction. The FR-LAD scheme has favorable properties of subcell shock capturing with the length scale of O (h / p) and superior preservation of high-order accuracy for smooth flows. Finally, LES of an overexpanded supersonic jet is performed to demonstrate its capability for practical applications. The proposed FR-LAD scheme can be an attractive candidate for LES or DNS of compressible flows involving shocks, contact discontinuities, turbulence, and their interactions on unstructured meshes. [ABSTRACT FROM AUTHOR]

Highlights • Positive feedback in regulatory circuits can trap system in control-robust behavior. • Analysis of ODE models is informed by analogies with Boolean systems. • Subsystem behavior can place bounds on the effectiveness of system interventions. Abstract We consider a dynamic framework frequently used to model gene regulatory and signal transduction networks: monotonic ODEs that are composed of Hill functions. We derive conditions under which activity or inactivity in one system variable induces and sustains activity or inactivity in another. Cycles of such influences correspond to positive feedback loops that are self-sustaining and control-robust, in the sense that these feedback loops "trap" the system in a region of state space from which it cannot exit, even if the other system variables are externally controlled. To demonstrate the utility of this result, we consider prototypical examples of bistability and hysteresis in gene regulatory networks, and analyze a T-cell signal transduction ODE model from the literature. [ABSTRACT FROM AUTHOR]

Highlights • A therapeutic game has a defined biological, behavioral or psychological mechanism. • Game design and game mechanics are mechanisms that can modify disease behaviour. • Lack of clinical evidence has prevented uptake of games as a digital medicine. • There is an unmet need for health specific game engines for clinical translation. • The IDEAL-GAMES framework is new methodology for game validation and testing. Abstract Health based games have significant potential as therapeutic interventions due to the inherent mechanisms associated with social and individual game play and their capacity for sensor integration, data capture analysis and patient feedback. Moreover, they are low cost and they can be deployed at the point of care across an evolving digital ecosystem. However, a robust evidence base to support their wider adoption as a clinical intervention for chronic diseases is lacking and significant methodological barriers exist for health games developers creating efficacious 'digital medicines'. Game design is complex and it must utilise validated game mechanics balanced with a creative and engaging game design. The aim of this review is therefore to outline the fundamental steps of game development for health professionals and to critically appraise the methodology for assessing health games as medical interventions. This requires (1) The adoption of clearly defined global language for health games development based on a targeted function as therapeutic agents. (2) The development of multidisciplinary teams with a broad portfolio of development and clinical skill sets. (3) The creation of health game engines specifically built to facilitate clinical game development. (4) Robust trial design and assessment of translational impact: If games are to be prescribed, their efficacy and toxicity must be based on a rigorous assessment of their use within a real world clinical environment. Trials for precision health games have specific challenges around blinding, learning curves, bias and confounding that are particularly problematic. We propose the adoption of the IDEAL-GAMES framework for game development that systematically assess and validates games through open registries. In conclusion we propose a new framework for assessing the robustness and clinical efficacy of games for health as clinical interventions in the clinical environment. [ABSTRACT FROM AUTHOR]

Abstract The work proposes a simple, efficient and unified approach to combat carbuncle phenomenon. The basic idea of this approach is to construct an affordable shock-stable item into numerical flux. This item is acquired by comparing three-wave Roe solver and two-wave HLL solver based on Roe linearization and the projection of difference term. It is consistent with shear viscosity and meantime restrained by the pressure-based sensing function for shear layer. The proposed enhancement is easy to implement and apply within Roe, HLLEM, HLLC, AUSM+, etc. Several well-known cases illuminate its shock robustness and potential application to hypersonic flows. [ABSTRACT FROM AUTHOR]

Abstract This paper addresses an on-off damping isolation regulated by a two-tuning-knob single-sensor controller. The switching law is based on a frequency range selector combined from foundation's velocity, foundation's displacement and two tuning knobs. The advantages of proposed controller include: simple static switching law, single sensor requirement, analytical optimizing, avoidance of chatter and robust effectiveness. In the numerical demonstration, the simplicity, robustness, and effectiveness of the proposed optimized controller is illustrated, considering noisy measurements, time delay, modeling error, practical integration and excitation amplitude. A simple experiment illustrates the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]