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Control inputs of fully-constrained cable-driven parallel robots (CDPRs) are constrained by the positiveness of the cables tension, as cables merely apply tensile forces. The positive tension distribution (PTD) in CDPRs is usually guaranteed with iterative optimization techniques, utilizing the redundant actuation of the CDPR. The iterative nature of the conventional PTD limits their real-time application since the worst-case computation time of the iterative methods is not predictable. Additionally, optimization methods are prone to model uncertainties. This paper addresses the PTD problem in the fully-constrained CDPRs with a control viewpoint. In the proposed approach, the PTD algorithm is an integral part of the controller, which explicitly generates positive values for the cables tension. To this aim, a saturation-type function is coupled with the controller, and its effect is compensated using a nonlinear disturbance observer (NDO). The stability of the proposed control scheme is also investigated in detail through Lyapunov's second method, considering a non-singular terminal sliding mode controller. Furthermore, the performance of the proposed methodology is compared with the conventional method for a six-degrees-of-freedom CDPR, in the presence of uncertainties. Finally, the effectiveness of the proposed control scheme is investigated through experiments.

Bulk La2O3-Ga2O3-ZrO2 (LGZ) glass and Tm3+/Yb3+ co-doped LGZ glasses were synthesized successfully using containerless technique. Raman spectra result reveals that the matrix sample possesses the low highest phonon energy of ∼ 642 cm–1. The glasses show good compatibility between large Abbe numbers (>31) and high refractive indices (nd > 1.93). Moreover, transmittance measurements reflect the glasses have high infrared transmittance of ∼ 81.9%, small OH– absorption coefficient and long mid-infrared cut-off wavelengths (∼7.5 μm). The surface morphology of host glass was characterized by SEM micrograph and EDS tests reflect that the doped compositions are distributed into the matrix glass homogeneously. The results of thermal analysis show that the glasses have good thermal properties (Tg > 769 °C). Excited by 980 nm laser, an intense 1810 nm fluorescence is obtained originating from the transition: 3F4 → 3H6 of Tm3+ ion, accompanied by upconversion emission. It can be observed that 1810 nm fluorescence has the highest intensity at 1 mol% Yb2O3 and owns broad full width at half-maximum (>245 nm), the luminescence intensity of 3F4 → 3H6 transition increases with rising temperature from 300 to 550 K. Furthermore, the value of energy transfer efficiency shows that Yb3+ can transfer energy to Tm3+ effectively. By fitting the attenuation curves, the lifetimes of 1810 and 474 nm emission can be acquired.

Checking uniform attractivity of a time-varying dynamic system without a strict Lyapunov function is challenging as it requires the characterization of the limiting behavior of a set of trajectories. In the context of hybrid nonlinear time-varying (NLTV) systems, characterizing such limiting or convergent behaviors is even harder due to the complexity stemming from both continuous-time variations as well as discrete-time jumps. In this work, an extension of the standard hybrid time domain is introduced to define limiting behaviors, using set convergence, when time approaches either positive infinity or negative infinity. In particular, it is shown how to characterize limiting behaviors under the condition that an output signal approaches zero. Such limiting behaviors and their associated limiting systems can be used to verify uniform global attractivity. Particularly, a generalization of the classic Krasovskii-LaSalle theorem is obtained for hybrid time-varying systems. Two examples are used to demonstrate the effectiveness of the results.

In this article, we mainly consider the existence of solutions and global Mittag-Leffler stability of delayed fractional-order coupled reaction-diffusion neural networks without strong connectedness. Using the Leary-Schauder's fixed point theorem and the Lyapunov method, some criteria for the existence of solutions and global Mittag-Leffler stability are given. Finally, the correctness of the theory is verified by a numerical example.

This work proposes a scalable gamma non-negative matrix network (SGNMN), which uses a Poisson randomized Gamma factor analysis to obtain the neurons of the first layer of a network. These neurons obey Gamma distribution whose shape parameter infers the neurons of the next layer of the network and their related weights. Upsampling the connection weights follows a Dirichlet distribution. Downsampling hidden units obey Gamma distribution. This work performs up-down sampling on each layer to learn the parameters of SGNMN. Experimental results indicate that the width and depth of SGNMN are closely related, and a reasonable network structure for accurately detecting brain fatigue through functional near-infrared spectroscopy can be obtained by considering network width, depth, and parameters.

A multivariable finite-time control algorithm is proposed for double integrator systems with matched disturbances. The remarkable feature of the developed method is to ensure the finite-time stability of the closed-loop system with uniformly bounded control signal with respect to arbitrary initial conditions. The convergence criteria is derived by using the Lyapunov-based techniques, and the upper bound of the control signal can be calculated based on the obtained criteria. Finally, the effectiveness of the proposed algorithm is confirmed by some numerical simulations.

This article addresses the dynamics and control problem of integrated position and attitude stabilization via manipulator actuation for spacecraft in proximity operations subject to external disturbances. Following recursive modeling philosophy, kinematics, and dynamics are first formulated for multiple-manipulator actuated coupled position and attitude system of spacecraft in the presence of external disturbances, where two vector factorizations are proposed to ensure a compact and explicit dynamic formulation, holding the inherent skew-symmetric property of the coefficient matrices. In view of the unconspicuous cascaded-like system structure, a nonlinear control scheme is designed following backstepping philosophy to drive the joints of manipulators causing reactions to robustly stabilize the spacecraft position and attitude. Toward this end, a reference trajectory prescribing the spacecraft motion is predesigned by means of polynomial functions ensuring well-behaviored performance. Then, as a key component, a second-order dynamic filter is constructed making use of the dynamics structure to generate the command joint motion capable of manipulator actuation. The rigorous closed-loop stability and robust performance analyses are undertaken within the Lyapunov framework. Moreover, three types of self-collisions possibly happening in the motion of multiple manipulators are considered and handled by three collision detection methods, which facilitates a collision-free trajectory shaping during the aforementioned reference trajectory design phase. Finally, numerical simulations are given to demonstrate the effect and robustness of the proposed control scheme.

Glycine functionalized activated carbon adsorption material (NOPAC-GLY-X) was successfully prepared by one-step thermal decomposition using agricultural waste navel orange peel as a precursor. Through batch adsorption experiments, it is found that the adsorption performance of Gd(III) on activated carbon can be significantly enhanced by glycine modification. The adsorption isotherms of the NOPACs conformed to the Langmuir isotherm model, and the maximum adsorption capacity of the activated carbon sample NOPAC-Gly-60 is approximately 48.5 mg/g. The Gd(III) adsorption capacity of navel orange peel activated carbon can be doubled after glycine modification, and the adsorption efficiency of gadolinium can reach 99% at pH=7. The physicochemical properties of the prepared adsorbents were characterized by BET, FTIR, EA, and XPS. The characterization test shows that the specific surface area of the sample increases from 1121 to 1523 m2/g, and the ratio of (N+O)/C increases from 10.8% to 30.0% by the glycine modification. After five cycles of adsorption-desorption, the adsorption capacity can still be maintained at 88% of the initial capacity. NOPAC-GLY-60 has excellent adsorption selectivity for Gd(III). With the obvious advantages of simple synthesis steps and low cost, the activated carbon modification method adopted in this study has great application value in the field of rare earth adsorption and recovery.

Inexpensive Cu2ZnZnS4 (CZTS) films using a two-step method have been developed for use in photovoltaic devices. Gavalnostatic electroplating precursors from environmentally friendly electrolytes and sulfurization were implemented in this method. Optimized sequential electrodepositions of Cu, Sn, and Zn films were carried out on molybdenum-coated glass, while the sulfurization of Mo/Cu/Sn/Zn precursors was performed at 500 °C or 550 °C in a sealed quartz tube filled with argon. The produced CZTS films were characterized in detail by photoelectrochemical measurements (PECMs, as photoelectrocatalytic detections), scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDX), Raman microspectroscopy, X-ray diffraction (XRD), and UV-Vis spectroscopy. PECMs revealed a photocurrent of “copper poor” CZTS sulfurized at 550 °C up to 1.5 mA/cm2, much higher than that of “copper rich” CZTS sulfurized at 550 °C (0.1 mA/cm2). SEM showed Sn particles were dispersed in the precursors, and the morphologies of “copper poor” CZTS showed less crevices and/or pinholes than those of “copper rich” CZTS. EDX analysis showed only Zn-loss could be found after the sulfurization. Raman imaging revealed the uniform composition of “copper poor” CZTS sulfurized at 550 °C, while “copper rich” CZTS processed at the same temperature contained binary compound SnS2 interposing the CZTS. The results of both XRD and Raman depicted that all CZTS films possessed kesterite structure. A direct band gap of about 1.45 eV for the produced CZTS film was determined from UV-Vis spectroscopy. All the measurements demonstrated that the CZTS film fabricated by galvanostatic electroplating meets the requirements for a light-absorbing layer and is a potential candidate for assembling CZTS solar cells. The J-V measurement of the resulting device confirms this assertion, and demonstrates a 5.0% conversion efficiency of the CZTS solar cell.

We present a novel Neural Embedding Spatio-Temporal (NEST) point process model for spatio-temporal discrete event data and develop an efficient imitation learning (a type of reinforcement learning) based approach for model fitting. Despite the rapid development of one-dimensional temporal point processes for discrete event data, the study of spatial-temporal aspects of such data is relatively scarce. Our model captures complex spatio-temporal dependence between discrete events by carefully design a mixture of heterogeneous Gaussian diffusion kernels, whose parameters are parameterized by neural networks. This new kernel is the key that our model can capture intricate spatial dependence patterns and yet still lead to interpretable results as we examine maps of Gaussian diffusion kernel parameters. The imitation learning model fitting for the NEST is more robust than the maximum likelihood estimate. It directly measures the divergence between the empirical distributions between the training data and the model-generated data. Moreover, our imitation learning-based approach enjoys computational efficiency due to the explicit characterization of the reward function related to the likelihood function; furthermore, the likelihood function under our model enjoys tractable expression due to Gaussian kernel parameterization. Experiments based on real data show our method's good performance relative to the state-of-the-art and the good interpretability of NEST's result.

Ranked keyword search has gained increasing attention due to its attractive advantages such as flexibility and accessibility. However, most existing ranked keyword search schemes ignore the semantic associations between the documents and queries. To solve this challenging issue in cloud-assisted edge computing, we first design the Semantic-aware Ranked Multi-keyword \textbf{S}earch (SRMS) scheme by adopting the Latent Dirichlet Allocation (LDA) topic model and the Chinese Remainder Theorem (CRT)-based secret sharing mechanism. Considering that the cloud server may be malicious, we implement a basic verification mechanism in SRMS to verify the correctness and completeness of search results and extend this verification mechanism in cloud-assisted edge computing scenarios. Formal security analysis proves that SRMS and extended result verification mechanisms are secure in both the known ciphertext model and the known background model. Extensive experiments using the real-world dataset demonstrate that SRMS is efficient and feasible in practical applications.

This article studies an event-triggered asynchronous output regulation problem (EAORP) for networked switched systems (NSSs) with unstable switching dynamics (USDs) including all modes unstable and partial switching instants destabilization, which means that the Lyapunov function increases both on the activation intervals of all subsystems and at some switching instants. First, a memory-based mode-compared event-triggered mechanism for switched systems is proposed to effectively shorten asynchronous intervals, which employs historical sampled outputs and compares the mode of the current sampled instant and the adjacent sampled instant. Then, the maximum average dwell time for a novel switching signal is derived with a constraint on the ratio of total destabilizing switchings to total stabilizing switchings, which relaxes the requirement of the regular arrangement of destabilizing and stabilizing switchings. Moreover, with the help of different coordinate transformations in the EAORP, the discretized Lyapunov functions are no longer needed when synthesizing the NSSs with USDs, and the asynchronous switching situation is also discussed. Afterward, by designing a dynamic output feedback controller, sufficient conditions are given to solve the EAORP for NSSs with USDs subject to network-induced delays, packet disorders, and packet losses. Finally, the effectiveness of the proposed methods is verified via a switched RLC circuit.

This work proposes a nonparametric prior induced deep sum-logarithmic-multinomial mixture (DSLMM) model to detect pilots' cognitive states through the developed brain power map. DSLMM uses multinormal distribution to infer the latent variable of each neuron in the first layer of the network. These latent variables obeyed a sum-logarithmic distribution that is backpropagated to its observation vector and the number of neurons in the next layer. Multinormal distribution is used to segment the extended observation vector to form a matrix associated with the width of the next layer. This work also proposes an adaptive topic-layer stochastic gradient Riemann (ATL-SGR) Markov chain Monte Carlo (MCMC) inference method to learn its global parameters without heuristic assumptions. The experimental results indicate that DSLMM can extract more probability distribution contained in the brain power map layer by layer, and achieve higher pilot cognition detection accuracy.

This article is concerned with the intermittent estimator-based mixed passive and H∞ control for the high-speed train (HST) with multiple noises, actuator stochastic fault, and sensor packet loss. First, an intermittent estimator is designed to track the undetectable status of HSTs in response to only partial information available due to sensor failures. Then, two different stability criteria are developed by adopting two different Lyapunov function strategies. Simultaneously, in order to reduce the control cost and accelerate the convergence time, two different algorithms are designed. It is worth emphasizing that different from the existing results of HST subject to actuator fault, this article adopts a more flexible fault representation mode, namely, semi-Markov switching mode, which is more in line with the practical background and has a higher valuable application. Especially, the Lyapunov function designed in this article can drive the system state to decrease monotonically in both the ``working interval'' and the ``rest interval,'' so as to avoid the phenomenon of state impulsive jump. Finally, through the test of HST experimental value of Japan's Shinkansen, the simulation results show the effectiveness and rationality of the proposed control method and also make a comparative analysis with related works, to prove the advantages of the control technology proposed in this article.

This article focuses on the vibration reducing and angle tracking problems of a flexible unmanned spacecraft system subject to input nonlinearity, asymmetric output constraint, and system parameter uncertainties. Using the backstepping technique, a boundary control scheme is designed to suppress the vibration and regulate the angle of the spacecraft. A modified asymmetric barrier Lyapunov function is utilized to ensure that the output constraint is never transgressed. Considering the system robustness, neural networks are used to handle the system parameter uncertainties and compensate for the effect of input nonlinearity. With the proposed adaptive neural network control law, the stability of the closed-loop system is proved based on the Lyapunov analysis, and numerical simulations are carried out to show the validity of the developed control scheme.

In this article, an adaptive neural safe tracking control scheme is studied for a class of uncertain nonlinear systems with output constraints and unknown external disturbances. To allow the output to stay in the desired output constraints, a boundary protection approach is developed and utilized in the output constrained problem. Since the generated output constraint trajectory is piecewise differentiable, a dynamic surface method is utilized to handle it. For the purpose of approximating the system uncertainties, a radial basis function neural network (RBFNN) is adopted. Under the output of the RBFNN, the disturbance observer technology is employed to estimate the unknown compound disturbances of the system. Finally, the Lyapunov function method is utilized to analyze the convergence of the tracking error. Taking a two-link manipulator system, as an example, the simulation results are presented to illustrate the feasibility of the proposed control scheme.

In this article, a novel normal-skew mixture (NSM) distribution is presented to model the normal and/or heavy-tailed and/or skew nonstationary distributed noises. The NSM distribution can be formulated as a hierarchically Gaussian presentation by leveraging a Bernoulli distributed random variable. Based on this, a novel robust Kalman filtering framework can be developed utilizing the variational Bayesian method, where the one-step prediction and measurement-likelihood densities are modeled as NSM distributions. For implementation, several exemplary robust Kalman filters (KFs) are derived based on some specific cases of NSM distribution. The relationships between some existing robust KFs and the presented framework are also revealed. The superiority of the proposed robust Kalman filtering framework is validated by a target tracking simulation example.

In this paper, for the mapping of a sphere into a compact orientable manifold S n → M , n ⩾ 1 , we solve the problem of determining whether it represents a nontrivial element in the homotopy group of the manifold π n ( M ) πn(M ). For this purpose, we consistently use the theory of iterated integrals developed by K.-T. Chen. It should be noted that the iterated integrals as repeated integration were previously meaningfully used by Lappo-Danilevsky to represent solutions of systems of linear differential equations and by Whitehead for the analytical description of the Hopf invariant for mappings f : S 2 n - 1 → S n , n ⩾ 2 . We give a brief description of Chen’s theory, representing Whitehead’s and Haefliger’s formulas for the Hopf invariant and generalized Hopf invariant. Examples of calculating these invariants using the technique of iterated integrals are given. Further, it is shown how one can detect any element of the fundamental group of a Riemann surface using iterated integrals of holomorphic forms. This required to prove that the intersection of the terms of the lower central series of the fundamental group of a Riemann surface is a unit group.

Sulfolane is an important aprotic polar solvent. Liquid-liquid equilibrium (LLE) data for the ternary systems of water + 1,2-dichloroethane + sulfolane were measured at temperatures of 288.15, 298.15 and 308.15 K under the atmospheric pressure. The distribution coefficient and selectivity were determined from the measured LLE data, which showed that 1,2-dichloroethane is a suitable extractant for the recovery of sulfolane from its aqueous solution. The nonrandom two-liquid (NRTL) model and the universal quasi-chemical (UNIQUAC) model were utilized to correlate the experimental LLE data. The low values of RMSD indicated that the ternary system could be fitted well by the NRTL and UNIQUAC models. The consistency of the binary interaction parameters for the two thermodynamic models obtained was confirmed by the topological information contained in the Gibbs energy of mixing function (GM/RT).

When viewing omnidirectional images (ODIs), viewers can access different viewports via head movement (HM), which sequentially forms head trajectories in spatial-temporal domain. Thus, head trajectories play a key role in modeling human attention on ODIs. In this paper, we establish a large-scale dataset collecting 21,600 head trajectories on 1,080 ODIs. By mining our dataset, we find two important factors influencing head trajectories, i.e., temporal dependency and subject-specific variance. Accordingly, we propose a novel approach integrating hierarchical Bayesian inference into long short-term memory (LSTM) network for head trajectory prediction on ODIs, which is called HiBayes-LSTM. In HiBayes-LSTM, we develop a mechanism of Future Intention Estimation (FIE), which captures the temporal correlations from previous, current and estimated future information, for predicting viewport transition. Additionally, a training scheme called Hierarchical Bayesian inference (HBI) is developed for modeling inter-subject uncertainty in HiBayes-LSTM. For HBI, we introduce a joint Gaussian distribution in a hierarchy, to approximate the posterior distribution over network weights. By sampling subject-specific weights from the approximated posterior distribution, our HiBayes-LSTM approach can yield diverse viewport transition among different subjects and obtain multiple head trajectories. Extensive experiments validate that our HiBayes-LSTM approach significantly outperforms 9 state-of-the-art approaches for trajectory prediction on ODIs, and then it is successfully applied to predict saliency on ODIs.

Calibration of agent-based models (ABM) is an essential stage when they are applied to reproduce the actual behaviors of distributed systems. Unlike traditional methods that suffer from the repeated trial and error and slow convergence of iteration, this article proposes a new ABM calibration approach by establishing a link between agent microbehavioral parameters and systemic macro-observations. With the assumption that the agent behavior can be formulated as a high-order Markovian process, the new approach starts with a search for an optimal transfer probability through a macrostate transfer equation. Then, each agent's microparameter values are computed using mean-field approximation, where his complex dependencies with others are approximated by an expected aggregate state. To compress the agent state space, principal component analysis is also introduced to avoid high dimensions of the macrostate transfer equation. The proposed method is validated in two scenarios: 1) population evolution and 2) urban travel demand analysis. Experimental results demonstrate that compared with the machine-learning surrogate and evolutionary optimization, our method can achieve higher accuracies with much lower computational complexities.

Accurate remaining useful life (RUL) of batteries plays an imperative role in ensuring safe operations and avoiding catastrophic accidents. However, in practice, complicated working conditions bring challenges to accurate battery prognostics. In this study, an accelerated stress factors based nonlinear Wiener process model is proposed to enrich inadequate battery prognostic works at various operating conditions. To realize online individual battery prognostics, once a new measurement is available, the parameters of a state-space model constructed by the proposed model are posteriorly updated. Then, based on the Peukert law and the Arrhenius equation, two specific accelerated stress-relevant drift functions and their associated degradation models at different discharge rates and temperatures are respectively designed. Subsequently, RUL predictions are conducted using the proposed method. RUL predictions at different discharge rates and different temperatures demonstrate the accuracy and robustness of the proposed prognostic models. According to some general prognostic metrics, the proposed method is proved to be superior to four existing RUL prediction approaches.

In order to reduce the possibility of structural fatigue and increase the lifetime of conveying fluid pipe, transverse vibration must be effectively eliminated. In this work, using parallel nonlinear energy sinks (NESs), a passive vibration controller is proposed to dissipate the vibration energy of the conveying fluid pipe. A high-order model of the conveying fluid pipe-parallel NESs system, in the form of partial differential equation, is derived and then converted into a quadratic form model containing the gradient information of a convex function. Combining the energy disturbance technique and first order convexity characteristic, the exponential stability of the closed-loop system is proved, which addresses the effectiveness of the proposed parallel NESs. Then, numerical simulations are given to verify the theoretical results and to illustrate the advantages of parallel NESs comparing with single NES. Finally, the reliability of the proposed approach is preliminarily verified through experiment.

Oxygen-assisted high temperature solid-state reaction approach was employed for the fabrication of Pr activated Ba(Mg0.28Zr0.16Ta0.56)O3 transparent ceramic phosphor. Retiveld refinement of X-ray diffraction pattern was carried out to reveal the lattice parameters and crystal structural information. Under the blue-light excitation of λex=473 nm, the phosphor exhibits a sharp intense red emission centered at 645 nm, accompanied by serval other weak peaks. PL evolution with temperature shows a significant luminescence quenching behavior, and the underlying multi-phonon interaction with optical center is revealed by proposing an unprecedent theoretical calculation work. Such a phonon effect is further confirmed from the red-shift of peak position with temperature, and the involved phonon energy of 50.7 meV is determined from Raman scattering measurement. More interestingly, the fitted results of transient-state PL spectra show a fluctuation of luminescence lifetime at various temperatures, further indicating a significant effect of phonon vibration in the system.

We prove the first polynomial bound on the number of monotonic homotopy moves required to tighten a collection of closed curves on any compact orientable surface, where the number of crossings in the curve is not allowed to increase at any time during the process. The best known upper bound before was exponential, which can be obtained by combining the algorithm of De Graaf and Schrijver [ J. Comb. Theory Ser. B , 1997] together with an exponential upper bound on the number of possible surface maps. To obtain the new upper bound, we apply tools from hyperbolic geometry, as well as operations in graph drawing algorithms—the cluster and pipe expansions—to the study of curves on surfaces. As corollaries, we present two efficient algorithms for curves and graphs on surfaces. First, we provide a polynomial-time algorithm to convert any given multicurve on a surface into minimal position. Such an algorithm only existed for single closed curves, and it is known that previous techniques do not generalize to the multicurve case. Second, we provide a polynomial-time algorithm to reduce any k -terminal plane graph (and more generally, surface graph) using degree-1 reductions, series-parallel reductions, and Δ Y -transformations for arbitrary integer k . Previous algorithms only existed in the planar setting when k ≤ 4, and all of them rely on extensive case-by-case analysis based on different values of k . Our algorithm makes use of the connection between electrical transformations and homotopy moves and thus solves the problem in a unified fashion.

We study the asymptotic behaviour of eigenvalues and eigenfunctions of 2D vibrating systems with mass density perturbed in a vicinity of closed curves. The threshold case in which resonance frequencies of the membrane and thin inclusion coincide or closely situated is investigated. The perturbed eigenvalue problem can be realized as a family of self-adjoint operators acting on varying Hilbert spaces. However the so-called limit operator which is ultimately responsible for the asymptotics of eigenvalues and eigenfunctions is non-self-adjoint and possesses the Jordan chains of length $2$. Apart from the lack of self-adjointness, the operator has non-compact resolvent. As a consequence, its spectrum has a complicated structure, for instance, the spectrum contains a countable set of eigenvalues with infinite multiplicity., Comment: 24 pages, 4 figures

In this paper, we prove the existence of nontrivial solutions and ground state solutions for the following planar Schrödinger–Poisson system with zero mass − Δ u + ϕ u = ( I α ∗ F ( u ) ) f ( u ) , x ∈ R 2 , Δ ϕ = u 2 , x ∈ R 2 , where α ∈ ( 0 , 2 ), I α : R 2 → R is the Riesz potential, f ∈ C ( R , R ) is of subcritical exponential growth in the sense of Trudinger–Moser. In particular, some new ideas and analytic technique are used to overcome the double difficulties caused by the zero mass case and logarithmic convolution potential.

Heterogeneous two-dimensional layered membranes reconstructed from natural or synthetic van der Waals materials enable novel ion transport mechanisms by coupling with the chemistry and optoelectron...

Design is an inseparable part of most scientific and engineering tasks, including real and simulation-based experimental design processes and parameter/hyperparameter tuning/optimization. Several model-based experimental design techniques have been developed for design in domains with partial available knowledge about the underlying process. This article focuses on a powerful class of model-based experimental design called the mean objective cost of uncertainty (MOCU). The MOCU-based techniques are objective-based, meaning that they take the main objective of the process into account during the experimental design process. However, the lack of scalability of MOCU-based techniques prevents their application to most practical problems, including large discrete or combinatorial spaces. To achieve a scalable objective-based experimental design, this article proposes a graph-based MOCU-based Bayesian optimization framework. The correlations among samples in the large design space are accounted for using a graph-based Gaussian process, and an efficient closed-form sequential selection is achieved through the well-known expected improvement policy. The proposed framework's performance is assessed through the structural intervention in gene regulatory networks, aiming to make the network away from the states associated with cancer.

This work investigates the stabilization problem of uncertain stochastic Markovian jump systems (MJSs) under communication constraints. To reduce the bandwidth usage, a discrete-time Markovian chain is employed to implement the stochastic communication protocol (SCP) scheduling of the sensor nodes, by which only one sensor node is chosen to access the network at each transmission instant. Moreover, due to the effect of amplitude attenuation, time delay, and random interference/noise, the transmission may be inevitably subject to the Rice fading phenomenon. All of these constraints make the controller only receive the fading signal from one activated sensor node at each instant. A merge approach is first used to deal with two Markovian chains; meanwhile, a compensator is designed to provide available information for the controller. By a compensator and mode-based sliding-mode controller, the resulting closed-loop system is ensured to be input-to-state stable in probability (ISSiP), and the quasisliding mode is attained. Moreover, an iteration optimizing algorithm is provided to reduce the convergence domain around the sliding surface via searching a desirable sliding gain, which constitutes an effective GA-based sliding-mode control strategy. Finally, the proposed control scheme is verified via the simulation results.

Magnetic nanoparticles (MNPs) have been widely studied for use in biomedical and industrial applications. The frequency dependence of the magnetization of magnetic nanoparticles is analyzed for different AC excitation fields. We employ a Fokker-Planck equation, which accurately describes AC magnetization dynamics and analyze the difference in AC susceptibility between Fokker-Planck equation and Debye model. Based on these results we proposed a simple, empirical AC susceptibility model. Simulation and experimental results showed that the proposed empirical model accurately describes AC susceptibility, and the AC susceptibility constructed with the proposed empirical equation based on Debye model agrees well with the measured results. Therefore, we can utilize the proposed empirical model in biomedical applications, such as the estimation of the hydrodynamic size and temperature, which is expected to apply to biologicals assays and hyperthermia.

We consider a stochastic EOQ-type model, with demand operating in a two-state random environment. This environment alternates between exponentially distributed periods of high demand and generally distributed periods of low demand. The inventory level starts at some level q, and decreases according to different compound Poisson processes during the periods of high demand and of low demand. Refilling of the inventory level to level q is required when level 0 is hit or when an expiration date is reached, whichever comes first. If such an event occurs during a high demand period, an order is instantaneously placed; otherwise, ordering is postponed until the beginning of the next high demand period. We determine various performance measures of interest, like the distribution of the inventory level at time t and of the inventory demand up to time t, the distribution of the time until refilling is required, the expected time between two refillings, the expected amount of discarded material and the expected total amount of material held in between two refillings, and the expected values of various kinds of shortages. For a given cost/revenue structure, we can thus determine the long-run average profit.

We introduce and study a new graph representation where vertices are embedded in three or more dimensions, and in which the edges are drawn on the projections onto the axis-parallel planes. We show that the complete graph on $n$ vertices has a representation in $\lceil \sqrt{n/2}+1 \rceil$ planes. In 3 dimensions, we show that there exist graphs with $6n-15$ edges that can be projected onto two orthogonal planes, and that this is best possible. Finally, we obtain bounds in terms of parameters such as geometric thickness and linear arboricity. Using such a bound, we show that every graph of maximum degree 5 has a plane-projectable representation in 3 dimensions., Accepted at CALDAM 2020

This research presents a dynamic modeling approach to predict flashover voltage (FOV) of polymer insulators. Pre-flashover conditions can be used to represent analytical formulations for different stages of dry band arcing activities on the surface of polluted insulator. In this study, experimental results of pollution flashover tests are used to develop a circuit model of pollution arcs based on Obenaus model. Estimation of circuit values is done by use of intelligent algorithm with no dependency on profile of insulators which is also more beneficial than typical analytical calculations. Then, separation and investigation of discharge and pollution voltage is done and proper classification is made. This classification is later used to predict pre-flashover conditions and pollution flashover. Gaussian model is used to make fitness of voltage of pollution layer and voltage of discharge on the surface of insulator. Gaussian representation of each stage in pollution flashover contains critical signals leading to complete discharge. Based on the obtained model, a flowchart is presented to predict dynamic stages of flashover. Comparison to various experimental data shows close correlations with the results of proposed model. An online monitoring system can be developed according to obtained results in order to prevent pollution flashover.

Model-based approaches to the verification of non-terminating Cyber-Physical Systems (CPSs) usually rely on numerical simulation of the System Under Verification (SUV) model under input scenarios of possibly varying duration, chosen among those satisfying given constraints. Such constraints typically stem from requirements (or assumptions) on the SUV inputs and its operational environment as well as from the enforcement of additional conditions aiming at, e.g., prioritising the (often extremely long) verification activity, by, e.g., focusing on scenarios explicitly exercising selected requirements, or avoiding vacuity in their satisfaction. In this setting, the possibility to efficiently sample at random (with a known distribution, e.g., uniformly) within, or to efficiently enumerate (possibly in a uniformly random order) scenarios among those satisfying the given constraints is a key enabler for the viability of the verification process, e.g., via simulation-based statistical model checking. Unfortunately, in case of non-trivial combinations of constraints, iterative approaches like Markovian random walks in the space of sequences of inputs in general fail in extracting scenarios according to a given distribution, and can be very inefficient to produce legal scenarios of interest. We show how, given a set of constraints on the input scenarios succinctly defined by finite memory monitors, a data structure (scenario generator) can be synthesised, from which any-horizon scenarios satisfying the input constraints can be efficiently extracted by (possibly uniform) random sampling or (randomised) enumeration. Our approach enables seamless support to virtually all simulation-based approaches to CPS verification, ranging from simple random testing to statistical model checking and formal (i.e., exhaustive) verification., Comment: 14 pages. IEEE Transactions on Software Engineering, 2021

Cutting tool condition directly affects machining quality and efficiency. In order to avoid severely worn tools used during machining process and fully release the remaining useful life in the meanwhile, a reliable evaluation method of remaining useful life of cutting tools is quite necessary. Due to the variation of cutting conditions, it is a challenge to predict remaining useful life of cutting tools by a unified model. In order to address this issue, this paper proposes a method for predicting the remaining useful life of cutting tools in variable cutting conditions based on Gaussian process regression model incorporated with tool wear mechanism, where the predicted value at adjacent moments is constrained to a linear relationship by the covariance matrix of Gaussian model based on the assumption of progressive tool wear process, so the wear process under continuous changing conditions can be modelled. In addition to that, the input feature space and the output of the model are also enhanced by considering the tool wear mechanism for improving prediction accuracy. Machining experiments are performed to verify the proposed method, and the results show that the proposed could improve the prediction of tool remaining useful life significantly.

In this research article, the perturbed nonlinear Schrodinger equation (P-NLSE) is examined by utilizing two analytical methods, namely the extended modified auxiliary equation mapping and the generalized Riccati equation mapping methods. Consequently, we establish several sorts of new families of complex soliton wave solutions such as hyperbolic functions, trigonometric functions, dark and bright solitons, periodic solitons, singular solitons, and kink-type solitons wave solutions of the P-NLSE. Using the mentioned methods, the results are displayed in 3D and 2D contours for specific values of the open parameters. The obtained findings demonstrate that the implemented techniques are capable of identifying the exact solutions of the other complex nonlinear evolution equations (C-NLEEs) that arise in a range of applied disciplines.

We propose a strategy for magnetic navigation of collective stem cell microrobots in blood environments under ultrasound Doppler imaging. The cell microrobots are fabricated through a coculture process of stem cells and iron microparticles, they have spheroidal structures and are actuated under external magnetic fields. The collective cell microrobots can be reversibly gathered and spread by adjusting the magnetic interaction, and these microrobots are able to exhibit collective motion in whole blood under rotating magnetic fields. Simulation results indicate that the induced blood flow around the collective pattern affects the motion of red blood cells, and experimental results show that Doppler signals are observed when emitting ultrasound waves to the microrobots. Due to the induced three-dimensional blood flow, Doppler signals can also be observed when the imaging plane is above the collective microrobots, which enables indirect localization when performing navigation on uneven surfaces. Moreover, collective patterns of different numbers of microrobots are formed in flowing conditions, and these collective biohybrid agents are actively navigated against flowing blood with a flow rate up to 4.5 mL/min (mean velocity: 5.97 mm/s). Our study investigates a strategy for pattern formation and navigation of collective microrobots under ultrasound Doppler imaging, demonstrating the integration of collective control approach and medical imaging holds great potential for real-time active delivery tasks.

Wear debris sensing is an important approach for machine condition monitoring. To date, different types of debris sensors have been developed, among which the magnetostatic inductive debris sensor is preferable for monitoring ferromagnetic debris, but the existing induced voltage model cannot be used to quantify wear debris in lube oil. Accordingly, a modeling method was proposed for the debris sensor based on a high-gradient magnetostatic field. First, a Gaussian curve is used to fit the magnetic field along the axis of the sensor. Then, time-harmonic electromagnetic field analysis and Fourier series decomposition are combined to establish an induced voltage model. Furthermore, the influence of the relative permeability, size, and velocity of wear debris and induction coil parameters on the output voltage is analyzed. The results show a linear relationship between the theoretical calculations and experimental results. Therefore, the proposed model can be used to realize quantitative analysis of wear debris, laying a foundation for optimizing the sensor structure, and extracting features from output voltage signals.

Expensive constrained optimization refers to problems where the calculation of the objective and/or constraint functions are computationally intensive due to the involvement of complex physical experiments or numerical simulations. Such expensive problems can be addressed by Gaussian process-assisted evolutionary algorithms. In many problems, the (single) objective and constraints are correlated to some extent. Unfortunately, existing works based on the Gaussian process for expensive constrained optimization treat the objective and multiple constraints as being statistically independent, typically for the ease of computation. To fill this gap, this paper investigates the correlation among the objective and constraints. To be specific, we model the correlation amongst the objective and constraint functions using a multi-task Gaussian process prior, and then mathematically derive a constrained expected improvement acquisition function that allows the correlation among the objective and constraints. The correlation between the objective and constraints can be captured and leveraged during the optimization process. The performance of the proposed method is examined on a set of benchmark problems and a real-world antenna design problem. On problems with high correlation amongst the objective and constraints, the experimental results show that leveraging the correlation yields improvements in both the optimization speed and the constraint-handling ability compared with the method which assumes the objective and constraints are statistically independent.

One of the pillar generative machine learning approaches in time series data study and analysis is the hidden Markov model (HMM). Early research focused on the speech recognition application of the model with later expansion into numerous fields, including video classification, action recognition, and text translation. The recently developed generalized Dirichlet HMMs have proven efficient in proportional sequential data modeling. As such, we focus on investigating a maximum a posteriori (MAP) framework for the inference of its parameters. The proposed approach differs from the widely deployed Baum-Welch through the placement of priors that regularizes the estimation process. A feature selection paradigm is also integrated simultaneously in the algorithm. For validation, we apply our proposed approach in the classification of dynamic textures and the recognition of infrared actions.

Motivated by the ABO issue of the blood bank system, in which the portions stored have constant shelf life, we consider two subsystems of perishable inventory. The two Perishable Inventory Subsystems-PIS A and PIS B, are correlated to each other through a one-way substitution of demands. Specifically, the input streams and the demand streams applied to each subsystem are four Poisson processes, which are independent of one another. However, if the shelf of PIS A (blood of type O) is empty of items, an arriving demand of type A is unsatisfied, since demand of type A cannot be satisfied by an item of type B (blood portions of type AB), but if the shelf of PIS B is empty of items, an arriving demand of type B is applied to PIS A, since demands of type B can be satisfied by both types. This one-way substitution of the issuing policy generates for PIS A a modulated Poisson demand process operating in a two-state non-Markovian environment. The performance analysis of PIS B is known from previous work. Thus, in this study we focus on the marginal performance analysis of PIS A. Based on a fluid formulation and a Markovian approximation for the one-way substitution demand process, we develop a unified approach to efficiently and accurately approximate the performance of the PIS A. The effectiveness of the approach is investigated by extensive numerical experiments.

This article investigates the fault-tolerant control problem for stochastic switched interval type-2 (IT2) fuzzy time-delayed uncertain systems based on unknown input observer synthesis, which can avoid uneasy measurement on the time derivative of output, and estimate unavailable or partially measurable states, including sensor and actuator faults accurately. First, a desired fuzzy observer is designed to ensure the observer-based dynamic error system mean-square exponentially stable with sufficient condition of a strict (l,ℏ,℘)-℧-dissipative performance, which is a unified framework of passivity, and H∞ provides results with less conservativeness. Then, we concentrate on stability analyses on dissipativity-based switched IT2 fuzzy systems with stochastic perturbation through linear matrix inequalities, Lyapunov function, free-weighting matrices, and average dwell time, discussing it according to different values of disturbance. Finally, simulation examples are listed to account for availability and effectiveness of the research methodology.

Chromic oxide (Cr2O3) monolayer is a promising alternative hydrogen evolution reaction (HER) catalyst compared with expensive platinum (Pt) due to its advantages such as low cost, large specific surface area, high reserves, and designability. In this study, the two practical strategies, strain engineering and transition metal (TM) doping (Mn, Fe, Zn, etc.), are proposed to activate the catalytic sites of Cr2O3 monolayer for the HER. The density functional theory (DFT) calculations demonstrate that the strained Cr2O3 monolayer can stimulate the HER activity with the Gibbs free energy of hydrogen adsorption (ΔGH∗) close to 0.09eV, which can be considered as a performable strategy to tune the HER catalytic behavior of Cr2O3 monolayer. For the TM doping, it also plays a role in the performance adjustment. These results provide a guideline to optimize the HER performance of Cr2O3 monolayer.

In medical image analysis (MIA) and computer-assisted surgery (CAS), aligning two multiple point sets (PSs) together is an essential but also a challenging problem. For example, rigidly aligning multiple point sets into one common coordinate frame is a prerequisite for statistical shape modelling (SSM). Accurately aligning the pre-operative space with the intra-operative space in CAS is very crucial to successful interventions. In this article, we formally formulate the multiple generalized point set registration problem (MGPSR) in a probabilistic manner, where both the positional and the normal vectors are used. The six-dimensional vectors consisting of both positional and normal vectors are called as generalized points. In the formulated model, all the generalized PSs to be registered are considered to be the realizations of underlying unknown hybrid mixture models (HMMs). By assuming the independence of the positional and orientational vectors (i.e., the normal vectors), the probability density function (PDF) of an observed generalized point is computed as the product of Gaussian and Fisher distributions. Furthermore, to consider the anisotropic noise in surgical navigation, the positional error is assumed to obey a multi-variate Gaussian distribution. Finally, registering PSs is formulated as a maximum likelihood (ML) problem, and solved under the expectation maximization (EM) technique. By using more enriched information (i.e., the normal vectors), our algorithm is more robust to outliers. By treating all PSs equally, our algorithm does not bias towards any PS. To validate the proposed approach, extensive experiments have been conducted on surface points extracted from CT images of (i) a human femur bone model; (ii) a human pelvis bone model. Results demonstrate our algorithm's high accuracy, robustness to noise and outliers.

This note proposes an adaptive second-order sliding mode (ASOSM) controller design by means of the Lyapunov method. The notable feature of the proposed algorithm is that it only needs boundedness of the uncertainties, while boundedness of the derivatives of uncertainties is not demanded. Under the proposed ASOSM control scheme, the gain can be dynamically tuned, which avoids gain overestimation. The finite-time stability of the closed-loop ASOSM dynamics is proved via the Lyapunov theory. Finally, the simulation results are shown to validate the theoretical analysis.

The eternal vertex cover problem is a variant of the classical vertex cover problem defined in terms of an infinite attacker–defender game played on a graph. In each round of the game, the defender reconfigures guards from one vertex cover to another in response to a move by the attacker. The minimum number of guards required in any winning strategy of the defender when this game is played on a graph G is the eternal vertex cover number of G , denoted by evc ( G ) . It is known that given a graph G and an integer k , checking whether evc ( G ) ≤ k is NP-hard. Further, it is known that for any graph G , mvc ( G ) ≤ evc ( G ) ≤ 2 mvc ( G ) , where mvc ( G ) is the vertex cover number of G . Though a characterization is known for graphs for which evc ( G ) = 2 mvc ( G ) , a characterization of graphs for which evc ( G ) = mvc ( G ) remained as an open problem, since 2009. We achieve such a characterization for a class of graphs that includes chordal graphs and internally triangulated planar graphs. For biconnected chordal graphs, our characterization leads to a polynomial time algorithm for precisely determining evc ( G ) and an algorithm for determining a safe strategy for guard movement in each round of the game using only evc ( G ) guards. Though the eternal vertex cover problem is only known to be in PSPACE in general, it follows from our new characterization that the problem is in NP for locally connected graphs, a graph class which includes all biconnected internally triangulated planar graphs. We also provide reductions establishing NP-completeness of the problem for biconnected internally triangulated planar graphs. As far as we know, this is the first NP-completeness result known for the problem for any graph class.