Unknown Sign Research Articles

Filter‐Based Average Dwell‐Time Tuning Approach for Adaptive Prescribed‐Time Tracking of Uncertain Switched Nonlinear Systems

ABSTRACTThis paper addresses neural‐network‐based adaptive prescribed‐time (PT) tracking for uncertain switched systems with unmatched nonlinearities. A continuously switched adaptive tuning mechanism for neural network learning is developed by applying the average dwell time (ADT). First, a neural‐network‐based PT tracking control design strategy using the ADT‐based adaptive tuning mechanism is established for switched nonlinear systems in strict‐feedback form. A novel adaptive dynamic surface controller is designed recursively using a practical finite‐time scaling function and continuously switched tuning parameters. The switched adaptive tuning laws for neural networks are structured to reduce the conservatism associated with common adaptive laws. Then, a filter‐based tuning approach is employed to ensure the continuity of switched adaptive parameters with ADT in the designed controller. The practical PT stability of the closed‐loop system is demonstrated based on the boundedness of the adaptive parameters. Building upon this foundation, the proposed PT design approach is extended to control switched pure‐feedback nonlinear systems, even in cases where control directions are unspecified. The unknown sign problem encountered with switched virtual and actual control coefficient functions is resolved in the PT control framework. It is shown that the PT performance bound of the tracking error can be reduced by selecting the design parameter of the scaling function. Finally, simulation results illustrate the merits of the proposed theoretical approach.

Exploring the Benefits and Applications of Video-Span Selection and Search for Real-Time Support in Sign Language Video Comprehension among ASL Learners

People learning American Sign Language (ASL) and practicing their comprehension skills will often encounter complex ASL videos that may contain unfamiliar signs. Existing dictionary tools require users to isolate a single unknown sign before initiating a search by selecting linguistic properties or performing the sign in front of a webcam. This process presents challenges in extracting and reproducing unfamiliar signs, disrupting the video-watching experience, and requiring learners to rely on external dictionaries. We explore a technology that allows users to select and view dictionary results for one or more unfamiliar signs while watching a video. We interviewed 14 ASL learners to understand their challenges in understanding ASL videos, strategies for dealing with unfamiliar vocabulary, and expectations for an in-situ dictionary system. We then conducted an in-depth analysis with 8 learners to examine their interactions with a Wizard-of-Oz prototype during a video comprehension task. Finally, we conducted a comparative study with 6 additional ASL learners to evaluate the speed, accuracy, and workload benefits of an embedded dictionary search feature within a video player. Our tool outperformed a baseline in the form of an existing online dictionary across all three metrics. The integration of a search tool and span selection offered advantages for video comprehension. Our findings have implications for designers, computer vision researchers, and sign language educators.

Neural network control of fractional-order chaotic systems with unknown control direction

In this paper, neural network control of fractional order chaotic systems (FOCSs) with input saturation and unknown sign of the controller gain is addressed by employing the Nussbaum function, where neural networks are utilized to model system uncertainties. To get rid of the limitation that reaching phase should be active before sliding motion in the traditional sliding mode control, a stable sliding surface is constructed. Then, by using the integer order Nussbaum gain control method, a novel controller with neural network sliding mode variable structure is designed. Finally, the practicality of the designed method is confirmed by a simulation experiment.

Robust state estimation via twisting observer

In this paper, we propose the twisting observer for robustly estimating the states of a second-order uncertain system. It is well-known that the twisting algorithm achieves the second-order sliding mode condition using the sign information of both the plant states. When all the states are not available, the twisting algorithm cannot be extended to the estimation problem directly. Hence, our proposed observer approximates this unknown sign term for the non-measurable state in the twisting observer with a delayed output-based switching function. It is shown that under some suitable gain conditions, the observer can guarantee any arbitrary accuracy of estimation error for some appropriate delay parameter which can be varied online. The advantage of this observer is that one needs to control only the delay parameter to achieve the desired steady-state accuracy, unlike the additional nonlinear functions found in the existing observers. The application of this observer to output feedback stabilization is also discussed. The separation principle for the closed-loop system is shown by designing the gains of controller and observer independently. Finally, we illustrate the design and performance of the proposed state observation scheme through a numerical example.

A bound on the cosmic opacity of unparticles from the CMB temperature

Unparticle cosmology gives an unconventional outlook on the dark sector of cosmology, increasingly challenging ΛCDM by H0-tension. This model derives from a finite temperature broken conformal symmetry of radiation, described by a non-radiative correction with unknown sign in energy density. This symmetry breaking has a sign ambiguity, in which corrections about the IR fixed point are normal or tachyonic. While the first has been ruled out in a recent study, the latter possesses a late-time temperature Tc≃4TCMB associated with ΩU≃1 (ΩU≃104ΩCMB), where TCMB denotes the temperature of the Cosmic Microwave Background (CMB). Thus exposed to the enormous heat bath of unparticles, TCMB is constrained by the astronomical age of the Universe Tu,0. The relative temperature shift ΔT/T in any heat exchange to the CMB is hereby limited to the uncertainty of a few percent in Tu,GC. This bounds the effective cross-section of unparticles interactions with CMB photons to σγU≲10−40m2=10−3nb. Assuming a standard cross-section across the broadband mass and energy spectrum of unparticles, a more stringent bound σγU≲10−3pb derives from COBE-FIRAS constraints on spectral distortions. The first is midway of photon-neutrino σγν and photon–photon cross-sections σγγ, the second is on par with σγν. These constraints put late-time unparticle cosmology, if present, at the edge of the standard model.

Resilient backstepping control for a class of switched nonlinear time-delay systems under hybrid cyber-attacks

In the present investigation, the tracking control problem is being addressed for a switched time-delay nonlinear nonstrict-feedback cyber-physical systems (CPSs). Since modern infrastructure applications depend greatly on cyber technologies, these CPSs are exposed to two types of collusive network attacks, including unknown false data injection (FDI) and denial-of-service (DoS) attacks. When a DoS attack is active, state variables are unavailable. Furthermore, when an FDI attack is launched, the general state is manipulated by the attacker. Inspired by these complications, a Lyapunov–Krasovskii (LK) candidate is employed to handle time delays, a neural network (NN) switched observer is applied to approximate unavailable system states under attacks, and a Nussbaum gain approach is implemented to deal with the unknown sign of the attack function. By using the common Lyapunov function, it is proved that all closed-loop system signals and tracking errors are bounded by the proposed resilient control. Eventually, two practical and numerical examples to corroborate the capability of the proposed scheme, are presented.

Neural network-based secure event-triggered control of uncertain industrial cyber-physical systems against deception attacks

This paper addresses the problem of secure event-triggered control is to guarantee the stability and security of a general class of industrial cyber-physical systems (CPSs) under limited resource budget and deception attacks. Note that the coupled data and the complicated structure of the CPS make it hard for the traditional control algorithms to fulfill the stability and security requirements. To this end, a neural network learning-based approximation algorithm is first proposed to separate the coupling influence, and then estimate the lumped uncertain nonlinearities. This is done by using the separated data as the input of the neural networks. Second, Nussbaum-type functions are presented to settle the unknown sign of the time-varying attack injection signal. Third, an event triggering mechanism is developed to reduce the communication load. Under the developed secure control laws, all the signals of the resulted closed-loop CPS are bounded. Finally, to validate the effectiveness of the proposed secure event-triggered control method, a benchmark control example for the one-link manipulator actuated by a DC motor is exploited.

Prespecifiable fixed‐time control for uncertain nonlinear system with input quantization

AbstractThis paper focuses on the global prespecifiable fixed‐time control problem of uncertain strict‐feedback nonlinear system with input quantization. In contrast to the existing fixed‐time control results, the signs of the control gains can be unknown in our paper. Unlike the traditional fixed‐time control schemes, some exponential functions are added into the virtual controllers to accelerate the convergence rate. Besides, some dynamic switching functions are also introduced to compensate the unknown control signs. Based on the novel regulate law, the switching functions can be regulated online to ensure the fixed‐time stability for the considered system. Furthermore, the settling time can be easily adjusted by selecting the controller parameters. Simulation results are provided to illustrate the effectiveness of the proposed control scheme.

Global regulation of feedforward nonlinear systems with unknown time-varying control coefficients and growth rate

In this paper, we consider a regulation problem for a class of feedforward nonlinear systems with unknown control coefficients and unknown growth rate. More specifically, the unknown control coefficients are assumed to be time-varying and belong to ranges with unknown upper and lower bounds. Due to the described control coefficients with uncertain feedfoward nonlinearities, our considered system is the natural extension of the related existing results. In solving our control problem, a new adaptive controller is derived by constructing a Lyapunov function in backstepping-like procedure and utilizing appropriate parameters based on the gain scaling technique and Nussbaum function. The uniquely designed exponents of a dynamic gain overcome the difficulties caused from the unknown sign and unknown ranges of the control coefficients and uncertain nonlinearities and thus play a key role in system regulation. We give the rigorous system analysis and simulation results of the numerical example to certify our control method.

Adaptive Uniform Performance Control of Strict-Feedback Nonlinear Systems With Time-Varying Control Gain

In this paper, we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients, which mainly includes the following steps. Firstly, by introducing several key transformation functions and selecting the initial value of the time-varying scaling function, the symmetric prescribed performance with global and semi-global properties can be handled uniformly, without the need for control re-design. Secondly, to handle the problem of unknown time-varying control coefficient with an unknown sign, we propose an enhanced Nussbaum function (ENF) bearing some unique properties and characteristics, with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer required. Thirdly, by utilizing the core-function information technique, the non-parametric uncertainties in the system are gracefully handled so that no approximator is required. Furthermore, simulation results verify the effectiveness and benefits of the approach.

Adaptive Neural Control for an Uncertain 2-DOF Helicopter System with Unknown Control Direction and Actuator Faults

In accordance with the rapid development of smart devices and technology, unmanned aerial vehicles (UAVs) have been developed rapidly. The two-degree-of-freedom helicopter system is a typical UAV that is susceptible to uncertainty, unknown control direction and actuator faults. Hence, a novel adaptive neural network (NN), fault-tolerant control scheme is proposed in this paper. Firstly, to compensate for the uncertainty, a radial-basis NN was developed to approximate the uncertain, unknown continuous function in the controlled system, and a novel weight-adaptive approach is proposed to save on computational cost. Secondly, a class of Nussbaum functions was chosen to solve the unknown-control-direction issue to prevent the effect of an unknown sign for the control coefficient. Subsequently, in response to the actuator faults, an adaptive parameter was designed to compensate for the performance loss of the actuators. Through rigorous Lyapunov analyses, the designed control scheme was proven to enable the states of the closed-loop system to be semi-globally uniformly bounded and the controlled system to be stable. Finally, we conducted a numerical simulation on Matlab to further verify the validity of the proposed scheme.

Management and outcomes of military penetrating neck injuries: An eleven-year retrospective case note review

IntroductionPenetrating Neck Injuries (PNIs) affected 3.2% of trauma patients attending US and UK deployed medical treatment facilities (MTFs) during the Iraq and Afghanistan conflicts. Injured military personnel requiring aeromedical evacuation for such injuries were managed at the Royal Centre for Defence Medicine (RCDM), Birmingham, UK. The aim of this paper was to review the management of PNI in both deployed MTFs and when evacuated back to the UK. Patients and methodsA retrospective case note review was performed of all military patients who sustained PNI whilst on deployment overseas, and who were subsequently evacuated to RCDM between March 2003 and December 2014. ResultsForty casualties who sustained PNI were identified, of which 28/40 (70%) sustained injury from explosive fragmentation, and 11/40 (28%) from gunshot wounds. Hard signs of PNI were present in 3/40 (7.5%) patients, soft signs in 14/40 (35%), no signs in 12/40 (30%), and unknown signs in 11/40 (28%) patients. Computed tomography angiography (CTA) was used in 39/40 (98%) patients, and was effective at ruling out significant injury, with 100% (29/29) of casualties with a negative CTA not developing vascular or aerodigestive injury. There were 9/29 (31%) patients who had surgical neck exploration despite both a negative CTA and absence of hard signs of PNI. There were 12/40 (30%) patients who required operative intervention at RCDM. ConclusionUK military surgeons in Role 3 MTFs had a low threshold for surgical exploration, even in the absence of CT findings or hard signs. This was likely due to the high-energy mechanisms responsible for military PNI, in addition to the limited availability of equipment and clinical expertise in visualising the larynx.

Adaptive security control of time-varying constraints nonlinear cyber-physical systems with false data injection attacks

In this article, an adaptive security control scheme is presented for cyber-physical systems (CPSs) suffering from false data injection (FDI) attacks and time-varying state constraints. Firstly, an adaptive bound estimation mechanism is introduced in the backstepping control design to mitigate the effect of FDI attacks. Secondly, to solve the unknown sign time-varying state-feedback gains aroused by the FDI attacks, a type of Nussbaum function is employed in the adaptive security control. Then, by constructing a barrier Lyapunov function, it can be ensured that all signals of controlled system are bounded and the time-varying state constraints are not transgressed. Finally, the provided simulation examples demonstrate the effectiveness of the proposed controller.

Event-Triggered Adaptive Bipartite Containment Control for Stochastic Multiagent Systems

In this article, the adaptive bipartite containment control problem is investigated for stochastic nonlinear multiagent systems (MASs) with an event-triggered mechanism. It is known that the dynamic surface control method suffers from the mismatch between the virtual controller and the filter output. To address this issue, a novel error compensator is designed. Meanwhile, motivated by their universal approximation capability, fuzzy-logic systems (FLSs) are employed to identify the plants’ unknown nonlinear characteristics. To reduce the communication overhead, a distributed event-triggered control scheme is designed based on an estimate of unknown gain sign’s reciprocal. Leveraging the stochastic Lyapunov stability theory and backstepping design technique, it is proved that 1) the output responses of followers converge to a convex hull formed by those of the leaders and their symmetric ones; 2) all signals in the closed-loop system are semiglobally uniformly ultimately bounded in probability (SGUUBP); and 3) there is no Zeno behavior. Finally, simulation results are presented to illustrate the effectiveness of the proposed method.

Selection of Two-Level Supersaturated Designs for Main Effects Models

An extensive literature is available on design selection criteria and analysis techniques for two-level supersaturated designs. The most notable design selection criteria are the popular -criterion, -criterion, and more recently, the var()-criterion, while the most notable analysis technique is the Gauss-Dantzig Selector. It has been observed that while the Gauss-Dantzig Selector is often the preferred analysis technique, differences in the screening performance of different designs are not captured well by any of the common design selection criteria. In addition, none of the criteria have any direct connection to the Gauss-Dantzig Selector. We develop two new design selection criteria inspired by large sample desiderata of the Gauss-Dantzig Selector. Then, using a multi-objective Pareto-based coordinate exchange algorithm, we find Pareto efficient designs. The obtained Pareto efficient designs perform better in about 85% of the considered cases as screening designs than the var()-optimal designs, especially when the true signs of effects are known. For the remaining 15% of the cases as well as for the unknown effect signs, the Pareto efficient designs perform at par with the var()-optimal designs.

Perichondritis auricularis: een zeldzame eerste presentatie van antineutrofiele cytoplasmatische antistoffen-geassocieerde vasculitis

Perichondritis auricularis: initial presentation of ANCA-associated vasculitis A 74-year-old woman consulted with persisting fever, tenderness of the ear and renal failure. The initial examination showed a systemic inflammatory process of unknown origin and clinical signs of perichondritis. Inflammatory and renal function deterioration under antibiotic treatment justified auto-immune serology and a kidney biopsy, which resulted in the diagnosis of PR3-ANCA (antineutrophil cytoplasmic antibodies) positive granulomatosis with polyangiitis (GPA). The patient was treated with corticoids and rituximab, under which she made a slow but steady recovery. In patients presenting with perichondritis and systemic inflammation, it remains important to include auto-immune diseases (such as GPA) into the differential diagnosis.

Taming the “stiff stiffness” of pressure work and equilibration in numerical schemes for compressible multi-fluid flows

Designing models and schemes for compressible multi-fluid flow is often considered as challenging when dealing with contrasted equations of state, low volume fractions, or high compression or expansion ratios. This is due to the potentially severe stiffness of pressure couplings, aggravated by (i) their quadratic multiplicity as they connect all energy reservoirs (ii) the entropy conditions to be preserved on each fluid, (iii) their potentially unknown signs, and (iv) their potential “stiff stiffness”, defined in the present work. Whether captured under pressure-equilibration or pressure-relaxation models—where fluids respectively share a common pressure or undergo a damped evolution towards a common pressure—the stiff terms of the evolution equations have always required dedicated numerical approaches, with sometimes mixed results depending on specific applications.Some broad general guidelines are here proposed to tame the stiff stiffness issues of the pressure terms. The framework is that of pressure-equilibrated average-field (or Euler–Euler) models, discretized with a splitting of momentum and internal energy equations: this makes the stiff terms behave locally as simple ODEs, amenable to integration by simple schemes. The present approach resorts to (partly) exponential integrators to provide explicit estimations of implicit pressures. The final explicit scheme, though not as robust as would its fully implicit version, displays a vastly improved resilience to stiffness.Numerical tests were carried out on strenuous versions of usual 1D shock tubes on two-fluid mixtures of air and water (described by ideal and stiffened gases). In view of the excellent results, further 1D tests in more extreme conditions were considered: free expansion to vacuum and shocks on zero pressure state (Noh’s test). At usual acoustic CFL values, expansion was robustly simulated for air volume fractions from less than 10−12 to up to nearly 1. Similarly, shocks on zero pressure states were simulated to different final states for air volume fractions as low as 10−8, with air density factors across shocks ranging between a few hundreds and hundredths depending on the choice of dissipation in each fluid. All tests were carried out on a previously developed Geometry, Energy, and Entropy Consistent (GEEC) scheme (Vazquez-Gonzalez et al., 2020).

Observer-based self-organizing adaptive fuzzy neural network control for non-linear, non-affine systems with unknown sign of control gain and dead zone: A case study of pneumatic actuators

In this paper, an observer-based self-organizing adaptive fuzzy neural network (OSOAFNN) control for non-linear, non-affine systems with the unknown sign of control gain and dead zone is presented. First, a reverse dead-zone compensator scheme to cope with the impact of dead-zone phenomenon existing in control input is investigated. Then, an observed-based control approach to address immeasurable states of the system is proposed, utilizing this approach all states of the system are not needed to be available. A self-organizing fuzzy neural network (SOFNN) technique is presented to approximate the non-linear and unknown function of the observer error dynamics. The proposed fuzzy neural network (FNN) model benefits from two main advantages: (1) the number of rules is automatically generated or pruned and (2) the parameters of antecedent and consequent part of SOFNN are updated through the hybrid tuning, simultaneously. Furthermore, the control law contains a Nussbaum function which deals with the unknown sign of control gain. As the system states are immeasurable, the strictly positive real (SPR) Lyapunov function to guarantee the closed-loop system stability, and tracking error convergence to zero is employed, as well as the boundedness of control parameters are assured through a projection law merged with adaptive law. Finally, the controller is practically implemented on a non-linear, non-affine pneumatic system with unknown dead zone which exploits just a sensor for output measuring. Experimental results show that the proposed controller has satisfactory performance in tracking different trajectories, and tracking error for desired signal case I and case II is limited to [−1.5, 1.5] and [−1,1]mm, respectively.

Musculoskeletal ultrasound as a screening-tool for rheumatoid arthritis: results of the \u201cRheuma-Truck\u201d screening and awareness initiative

ObjectiveTo evaluate musculoskeletal ultrasound (MSUS) as a screening tool for rheumatoid arthritis (RA) and osteoarthritis (OA) patients in a rheumatology-screening program.Patients and methodsTo raise awareness for rheumatic diseases, a mobile rheumatology office was deployed in different cities of Germany (“Rheuma-Truck”). Standardized questionnaire assessment, testing for rheumatoid factor and citrullinated peptide antibodies and medical student driven MSUS of the clinically dominant hand/foot including wrist, MCP-II, -III, -V, PIP-II, -III, MTP-II and -V were offered free of charge to the population. In case of suspicious results, a rheumatologist was consulted.ResultsIn MSUS, 192 of 560 selected volunteers (aged 18–89, mean 52.7 years; 72.9% female) had suspicious findings including synovitis or erosions primarily affecting the MTP-II (11.8%), dorsal wrist (8.9%), and MCP-II (7%). 354 of the 560 volunteers further visited a rheumatologist of whom 76 were diagnosed with RA. According to the ‘US7 Score’, a sum scores ≥ 5 was significantly predictive for RA (odds ratio (OR) 5.06; confidence interval (CI) 0.83–35.32). 313 volunteers displayed signs of OA including osteophytes, while MCP-II (36.2%), MCP-III (14.8%), and the wrist (10.5%) were mostly affected. Diagnosis of RA was favoured over OA if the wrist (OR 4.2; CI 1.28–13.95), MTP-II (OR 1.62; CI 1.0–2.6), and MCP-V (OR 2.0; CI 1.0–3.8) were involved.ConclusionMedical student driven MSUS by the ‘US7 Score’ can facilitate diagnosis of RA in rheumatology-screening programs due to the level of the score and the affected joints. A high rate of unknown OA signs was detected by MSUS. A mobile rheumatology office displays an opportunity to screen patients for RA and OA.

Distributed control of nonlinear systems with unknown time-varying control coefficients: A novel Nussbaum function approach

In this paper, distributed control of uncertain multi-agent systems (MAS) with completely unknown nonlinearities, unknown time-varying control coefficients and multiple unknown control directions is investigated. Firstly, a new theorem with a series of novel Nussbaum functions are presented, which solve the outstanding problem in control of nonlinear systems with unknown time-varying control coefficients and unknown signs. Secondly, global consensus of MAS with unknown time-varying control coefficients and unknown system nonlinearities under directed graph is firstly achieved, and the transient tracking performance is also guaranteed. Thirdly, a novel filter is proposed for each agent which does not require a prior knowledge of time derivative of leader. Finally, simulation results show the effectiveness of the proposed control schemes.