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Spatial interpolation is a class of estimation problems where locations with known values are used to estimate values at other locations, with an emphasis on harnessing spatial locality and trends. Traditional Kriging methods have strong Gaussian assumptions, and as a result, often fail to capture complexities within the data. Inspired by the recent progress of graph neural networks, we introduce Kriging Convolutional Networks (KCN), a method of combining the advantages of Graph Convolutional Networks (GCN) and Kriging. Compared to standard GCNs, KCNs make direct use of neighboring observations when generating predictions. KCNs also contain the Kriging method as a specific configuration. We further improve the model's performance by adding attention. Empirically, we show that this model outperforms GCNs and Kriging in several applications. The implementation of KCN using PyTorch is publicized at the GitHub repository: https://github.com/tufts-ml/kcn-torch.

The pixel size of the Infrared Atmospheric Sounding Interferometer (IASI) remote sensor is much smaller than the horizontal grid size of current Chemical Transport Models (CTMs). In order to assimilate the maximum of information from the IASI retrievals, we have increased the horizontal resolution of our model MOCAGE to be consistent with the IASI pixel size. Experiments are carried out with the Valentina data assimilation system using the standard and the high resolution versions of the model. Two resolutions of the horizontal Gaussian grid have been used for the model: with a T42 and a T170 triangular truncations. Our study is based on the combination of data from the IASI instrument and from the Microwave Limb Sounder (MLS), since this latter dataset allows the information to be spread through the whole atmospheric columns at a low computational cost. Two datasets of ozone super-observations have been constructed by averaging the IASI data on the two model grids. Direct model simulations without data assimilation first show that the increase of the horizontal resolution modifies the ozone smallest scale structures as well as the ozone meridional distribution. This modification results from a better representation of the vertical velocity with the T170 configuration. When the ozone assimilation is performed there is less influence of the horizontal resolution of the model. Nevertheless, in a general way, comparisons with independent data show large reductions of the ozone standard deviations when the resolution is increased. When the ozone assimilation is performed with the high resolution dataset, the high resolution model does not improve the ozone analysis compared to the one obtained with the same model resolution but with the low resolution IASI dataset. This result is due to the difficulty to combine IASI data and MLS data. For assimilating IASI data at high resolution the horizontal correlation length-scale has to be decreased to catch the small scale structures present in the dataset. By doing so the influence of the coarser resolution MLS data is decreased and part of the information brought on the vertical shape of the ozone profile is lost. It is concluded that it is essential to add information on the vertical distribution of ozone column when the IASI data is assimilated at a resolution close to the pixel size. Using IASI averaging kernels would likely improve the simulations, but the computational cost would be much higher. Alternatively, better results might be obtained by a careful tuning of the horizontal correlation length-scale.

Given a set $P$ of $n$ points in $\mathbb{R}^2$ and an input line $\gamma$ in $\mathbb{R}^2$, we present an algorithm that runs in optimal $\Theta(n\log n)$ time and $\Theta(n)$ space to solve a restricted version of the $1$-Steiner tree problem. Our algorithm returns a minimum-weight tree interconnecting $P$ using at most one Steiner point $s \in \gamma$, where edges are weighted by the Euclidean distance between their endpoints. We then extend the result to $j$ input lines. Following this, we show how the algorithm of Brazil et al. ("Generalised k-Steiner Tree Problems in Normed Planes", arXiv:1111.1464) that solves the $k$-Steiner tree problem in $\mathbb{R}^2$ in $O(n^{2k})$ time can be adapted to our setting. For $k>1$, restricting the (at most) $k$ Steiner points to lie on an input line, the runtime becomes $O(n^{k})$. Next we show how the results of Brazil et al. ("Generalised k-Steiner Tree Problems in Normed Planes", arXiv:1111.1464) allow us to maintain the same time and space bounds while extending to some non-Euclidean norms and different tree cost functions. Lastly, we extend the result to $j$ input curves., Comment: 31 pages (26 of content), 4 figures (last one as 3 subfigures). Minor corrections since publication: notably our analysis of the Brazil et al. k-steiner tree algorithm in section 4.1 (results unchanged), and a correction to some corollary statements to correct the space usage. Also added short discussion of how the algorithm still works when the MST of the input point set is not unique

Poisson Voronoi (PV) tessellations as artificial microstructures are widely used in investigations of material deformation behaviors. However, a PV structure usually describes a relative homogeneous field. This work presents a simple numerical method for generating 2D/3D artificial microstructures based on hierarchical PV tessellations. If grains/particles of a phase cover a large size span, the concept of “artificial phases” can be used to create a more realistic size distribution. From case to case, detailed microstructural features cannot be directly achieved by commercial or free softwares, but they are necessary for a deep or thorough study of the material deformation behavior. PV tessellations created in our process can fulfill individual requirements from material designs. Another reason to use PV tessellations is due to the limited experimental data. Concerning the application of PV microstructures, four examples are given. The FE models and results will be presented in consecutive works, i.e. “part II: applications”., Deutsche Forschungsgemeinschaft, Projekt DEAL

Mixing and evaporation processes play an important role in fluid injection and disintegration. Laser-induced thermal acoustics (LITA) also known as laser-induced grating spectroscopy (LIGS) is a promising four-wave mixing technique capable to acquire speed of sound and transport properties of fluids. Since the signal intensity scales with pressure, LITA is effective in high-pressure environments. By analysing the frequency of LITA signals using a direct Fourier analysis, speed of sound data can be directly determined using only geometrical parameters of the optical arrangement no equation of state or additional modelling is needed at this point. Furthermore, transport properties, like acoustic damping rate and thermal diffusivity, are acquired using an analytical expression for LITA signals with finite beam sizes. By combining both evaluations in one LITA signal, we can estimate mixing parameters, such as the mixture temperature and composition, using suitable models for speed of sound and the acquired transport properties. Finally, direct measurements of the acoustic damping rate can provide important insights on the physics of supercritical fluid behaviour., Deutsche Forschungsgemeinschaft, Projekt DEAL

In the autonomous ocean monitoring task, the sampling robot moves in the environment and accumulates data continuously. The widely adopted spatial modeling method - standard Gaussian process (GP) regression - becomes inadequate in processing the growing sensing data of a large size. To overcome the computational challenge, this paper presents an environmental modeling framework using a sparse variant of GP called streaming sparse GP (SSGP). The SSGP is able to handle streaming data in an online and incremental manner, and is therefore suitable for long-term autonomous environmental monitoring. The SSGP summarizes the collected data using a small set of pseudo data points that best represent the whole dataset, and updates the hyperparameters and pseudo point locations in a streaming fashion, leading to high-quality approximation of the underlying environmental model with significantly reduced computational cost and memory demand., Proceedings of OCEANS 2019, SEATTLE

Let $\mathcal{M}$ be a $\sigma$-finite von Neumann algebra, equipped with a normal faithful state $\varphi$, and let $\mathcal{A}$ be maximal subdiagonal algebra of $\mathcal{M}$. We prove Stein-Weiss type interpolation theorem of Haagerup noncommutative $H^{p}$-spaces associated with $\A$., Comment: 16. arXiv admin note: text overlap with arXiv:2209.08542

ObjectiveTo investigate gender differences in the use of diagnostic and therapeutic procedures for acute ischaemic heart disease (IHD) in Chinese adults and assess whether socioeconomic or health system factors contribute to such differences.MethodsIn 2004–2008, the China Kadoorie Biobank recruited 512 726 adults from 10 diverse areas in China. Data for 38 928 first hospitalisations with IHD (2911 acute myocardial infarction (AMI), 9817 angina and 26 200 other IHD) were obtained by electronic linkage to health insurance records until 31 December 2016. Multivariate Poisson regression models were used to estimate women-to-men rate ratios (RRs) of having cardiac enzyme tests, coronary angiography and coronary revascularisation.ResultsAmong the 38 928 individuals (61% women) with IHD admissions, women were less likely to have AMI (5% vs 12%), but more likely to have angina (26% vs 24%) or other IHD (69% vs 64%). For admissions with AMI, there were no differences in the use of cardiac enzymes between women and men (RR=1.00; 95% CI, 0.97 to 1.03), but women had lower use of coronary angiography (0.80, 0.68 to 0.93) and coronary revascularisation (0.85, 0.74 to 0.99). For angina, the corresponding RRs were: 0.97 (0.94 to 1.00), 0.66 (0.59 to 0.74) and 0.56 (0.47 to 0.67), respectively; while for other IHD, they were 0.97 (0.94 to 1.00), 0.87 (0.76 to 0.99) and 0.61 (0.51 to 0.73), respectively. Adjusting for socioeconomic and health system factors did not significantly alter the women-to-men RRs.ConclusionsAmong Chinese adults hospitalised with acute IHD, women were less likely than men to have coronary angiography and revascularisation, but socioeconomic and health system factors did not contribute to these differences.

Solving inverse problems, where we find the input values that result in desired values of outputs, can be challenging. The solution process is often computationally expensive and it can be difficult to interpret the solution in high-dimensional input spaces. In this paper, we use a problem from additive manufacturing to address these two issues with the intent of making it easier to solve inverse problems and exploit their results. First, focusing on Gaussian process surrogates that are used to solve inverse problems, we describe how a simple modification to the idea of tapering can substantially speed up the surrogate without losing accuracy in prediction. Second, we demonstrate that Kohonen self-organizing maps can be used to visualize and interpret the solution to the inverse problem in the high-dimensional input space. For our data set, as not all input dimensions are equally important, we show that using weighted distances results in a better organized map that makes the relationships among the inputs obvious., 16 figures and 4 tables

Titan longitudinal librations are dependent on the satellite internal structure and the elastic behavior of the surface. The elastic deformation of the surface is related to the perturbing potential through the Love theory. In a previous paper, we described the deformation as a response to the tidal potential exerted by Saturn at orbital frequency. Here we improve the tidal deformation reponse by including the effect of the libration angle and the orbital perturbations. We then provide the libration amplitudes associated with the rotational model of a tidally deformed three-layer Titan evolving on a non-Keplerian orbit.

This study explores our ability to simulate the atmospheric chemistry stemming from isoprene emissions in pristine and polluted regions of the Amazon basin. We confront two atmospheric chemistry models – a global, Eulerian chemistry-climate model (UM-UKCA) and a trajectory-based Lagrangian model (CiTTyCAT) – with recent airborne measurements of atmospheric composition above the Amazon made during the SAMBBA campaign of 2012. The simulations with the two models prove relatively insensitive to the chemical mechanism employed; we explore one based on the Mainz Isoprene Mechanism, and an updated one that includes changes to the chemistry of first generation isoprene nitrates (ISON) and the regeneration of hydroxyl radicals via the formation of hydroperoxy-aldehydes (HPALDS) from hydroperoxy radicals (ISO2). In the Lagrangian model, the impact of increasing the spatial resolution of trace gas emissions employed from 3.75° × 2.5° to 0.1° × 0.1° varies from one flight to another, and from one chemical species to another. What consistently proves highly influential on our simulations, however, is the model framework itself – how the treatment of transport, and consequently mixing, differs between the two models. The lack of explicit mixing in the Lagrangian model yields variability in atmospheric composition more reminiscent of that exhibited by the measurements. In contrast, the combination of explicit (and implicit) mixing in the Eulerian model removes much of this variability but yields better agreement with the measurements overall. We therefore explore a simple treatment of mixing in the Lagrangian model that, drawing on output from the Eulerian model, offers a compromise between the two models. We use this Lagrangian/Eulerian combination, in addition to the separate Eulerian and Lagrangian models, to simulate ozone at a site in the boundary layer downwind of Manaus, Brazil. The Lagrangian/Eulerian combination predicts a value for an AOT40-like accumulated exposure metric of around 1000 ppbv h, compared to just 20 ppbv h with the Eulerian model. The model framework therefore has considerable bearing on our understanding of the frequency at which, and the duration for which, the rainforest is exposed to damaging ground-level ozone concentrations.

Assessments with a large amount of small, similar, or often repetitive tasks are being used in educational, neurocognitive, and psychological contexts. For example, respondents are asked to recognize numbers or letters from a large pool of those and the number of correct answers is a count variable. In 1960, George Rasch developed the Rasch Poisson counts model (RPCM) to handle that type of assessment. This article extends the RPCM into the world of diagnostic classification models (DCMs) where a Poisson distribution is applied to traditional DCMs. A framework of Poisson DCMs is proposed and demonstrated through an operational dataset. This study aims to be exploratory with recommendations for future research given in the end.

This work focuses on the numerical solution of hyperbolic conservations laws (possibly endowed with a source term) using the Active Flux method. This method is an extension of the finite volume method. Instead of solving a Riemann Problem, the Active Flux method uses actively evolved point values along the cell boundary in order to compute the numerical flux. Early applications of the method were linear equations with an available exact solution operator, and Active Flux was shown to be structure preserving in such cases. For nonlinear PDEs or balance laws, exact evolution operators generally are unavailable. Here, strategies are shown how sufficiently accurate approximate evolution operators can be designed which allow to make Active Flux structure preserving / well-balanced for nonlinear problems.

We introduce a natural temporal analogue of Eulerian circuits and prove that, in contrast with the static case, it is NP-hard to determine whether a given temporal graph is temporally Eulerian even if strong restrictions are placed on the structure of the underlying graph and each edge is active at only three times. However, we do obtain an FPT-algorithm with respect to a new parameter called interval-membership-width which restricts the times assigned to different edges; we believe that this parameter will be of independent interest for other temporal graph problems. Our techniques also allow us to resolve two open question of Akrida, Mertzios and Spirakis [CIAC 2019] concerning a related problem of exploring temporal stars. Furthermore, we introduce a vertex-variant of interval-membership-width (which can be arbitrarily larger than its edge-counterpart) and use it to obtain an FPT-time algorithm for a natural vertex-exploration problem that remains hard even when interval-membership-width is bounded., Extended abstract of this paper appeared in IWOCA 2021: Combinatorial Algorithms pp 107-121 (doi: https://doi.org/10.1007/978-3-030-79987-8_8 )

Advection of trace species (tracers), also called tracer transport, in models of the atmosphere and other physical domains is an important and potentially computationally expensive part of a model's dynamical core (dycore). Semi-Lagrangian (SL) advection methods are efficient because they permit a time step much larger than the advective stability limit for explicit Eulerian methods. Thus, to reduce the computational expense of tracer transport, dycores often use SL methods to advect passive tracers. The class of interpolation semi-Lagrangian (ISL) methods contains potentially extremely efficient SL methods. We describe a set of ISL bases for element-based transport, such as for use with atmosphere models discretized using the spectral element (SE) method. An ISL method that uses the natural polynomial interpolant on Gauss-Legendre-Lobatto (GLL) SE nodes of degree at least three is unstable on the test problem of periodic translational flow on a uniform element grid. We derive new alternative bases of up to order of accuracy nine that are stable on this test problem; we call these the Islet bases. Then we describe an atmosphere tracer transport method, the Islet method, that uses three grids that share an element grid: a dynamics grid supporting, for example, the GLL basis of degree three; a physics grid with a configurable number of finite-volume subcells per element; and a tracer grid supporting use of our Islet bases, with particular basis again configurable. This method provides extremely accurate tracer transport and excellent diagnostic values in a number of validation problems. We conclude with performance results that use up to 27,600 NVIDIA V100 GPUs on the Summit supercomputer.

In this article, a new event-triggered $H_{∞}$ load frequency control (LFC) approach with dynamic triggered algorithm (DTA) for multi-area nonlinear power systems (NPSs) based on non-fragile proportional integral control (NPI-control) strategy is addressed. Firstly, different from the existing linear single-area LFC model for power systems, an improved nonlinear multi-area model with the performance of large-scale adjustment frequency fluctuation is constructed by considering the phenomenon of overshoots and long-term oscillations. Due to the existence of control uncertainty, it is the first time that the NPI-control scheme is applied to LFC approach for NPSs. Then, the DTA is proposed to adjust the dynamic event-triggered parameters, which reduces the occupation of communication bandwidth and the data computation of NPSs. Furthermore, a modified quadratic form with time-varying matrix and two-side closed functional method are adopted to construct the relaxed Lyapunov-Krasovskii functional, where some slack matrices are unnecessarily positive definite. Based on Lyapunov method, some less-conservatism stability criteria are derived. Utilizing the linear matrix inequality toolbox, the allowable upper bound of time-varying delays and the NPI-controller are obtained. Finally, a numerical example is presented to demonstrate the availability of the approach developed in this work.

Experimental and numerical studies are carried out to validate the potential of opposing Plasma Synthetic Jet (PSJ) for drag reduction for a hemisphere. Firstly, flow field changes of opposing PSJ are analyzed by comparing the experimental schlieren images and simulation results in a supersonic free stream of Mach number 3. As PSJ is a kind of unsteady pulsed jet, the shock standoff distance increases initially and then decreases under the control of PSJ, which corresponds to the change of the strength of PSJ. Accordingly, the amount of drag reduction of the hemisphere increases initially and then decreases. It is found that there is a short period of “drag rise” during the formation of PSJ before the drag reduction, which is induced by the generation of normal shock waves and the area difference of the cavity wall of PSJ Actuator (PSJA). Secondly, the effects of five parameters, including exit diameter, discharge energy of PSJA, Mach number, static pressure of incoming flow and angle of attack, on drag reduction of opposing PSJ were studied in detail by using numerical method. It is found that the Maximum Pressure Ratio (MPR) has a significant impact on the average drag reduction for a configuration-determined PSJA. For the configuration selected in this study, the flow field of opposing PSJ shows typical Short Penetration Mode (SPM) in a control cycle of PSJ when the MPR is less than 0.89. However, the flow field shows typical Long Penetration Mode (LPM) at some time when the MPR is bigger than 0.89. Relatively better drag reduction is achieved in this case.

In this article, we apply a high-order control to a dynamical system with uncertainty. There are two characteristics. First, the uncertain part, which is time-varying but bounded, is described in a fuzzy aspect. Specifically, the uncertainty lies within a fuzzy set and the bound is regarded as a fuzzy number. Second, the systems are uniformly bounded and uniformly ultimately bounded with a deterministic controller based on the Lyapunov theory. To obtain better system performance and lower control input, we apply the noncooperative game theory to optimize the parameters by establishing a Nash game. Then, the D-operation is proposed for the uncertainty related to fuzzy numbers. Finally, we perform the numerical simulations of the steering-by-wire system for verification.

The current study deals with exact soliton solutions for Schrodinger-Hirota (SH) equation via two modified integration methods. Those methods are known as the improved ( G ′ / G ) -expansion method and the Kudryashov method. This model is a generalized version of the nonlinear Schrodinger (NLS) equation with higher order dispersion and cubic nonlinearity. It can be considered as a more accurate approximation than the NLS equation in explaining wave propagation in the ocean and optical fibers. A novel derivative operator named as the conformable truncated M-fractional is used to study the above mentioned model. The obtained results can be used in describing the Schrodinger-Hirota equation in some better way. Moreover the obtained results are verified through symbolic computational software. Also, the obtained results show that the suggested approaches have broaden capacity to secure some new soliton type solutions for the fractional differential equations in an effective way. In the end, the results are also explained through their graphical representations.

Surface-enhanced Raman scattering (SERS) spectroscopy has been employed as a rapid analysis technology for food security inspection recently. Nowadays, it is still a great challenge to rapidly quantify multiple trace antibiotics potentially abused in aquaculture industry. In this work, a magnetic Ti3C2Tx/Fe3O4/Ag substrate was prepared for the development of a reliable rapid SERS quantification method for multiple trace sulfonamides in aquatic products. This magnetic substrate had good uniformity, reproducibility, stability and SERS activity. Moreover, this substrate could integrate the magnetic separation-enrichment and matrix clean-up without cross contamination, which endowed it with good selectivity and anti-interference capability during real sample analysis. The electromagnetic enhancement and chemical enhancement mechanism of this magnetic substrate were studied in detail to reveal its good separation-enrichment performance and SERS activity. Finally, a rapid SERS quantification method was established and practically applied for trace phthalic sulfathiazole (PST) and silver sulfadiazine (SSD) in aquatic products by using Ti3C2Tx/Fe3O4/Ag magnetic substrates. Trace PST and SSD could be actually detected and quantified as 55.9 μg/kg and 64.0 μg/kg in aquatic products respectively. Good recoveries of 83.9%-116% with relative standard deviations (RSDs) of 0.5%-3.2% for PST and 80.2%-102% with RSDs of 1.3%-5.8% for SSD were obtained. This work proposed an efficient and reliable method for rapid quantification of trace multiple sulfonamides in complex aquatic samples during food security inspection.

It is important to accurately obtain the motion information of the ego vehicle and surrounding vehicles for autonomous vehicles to plan safe trajectories in complicated traffic environments. In this paper, a LiDAR-based estimation method is developed to simultaneously identify the pose and the velocity information of an ego vehicle and its surrounding moving obstacles. Specifically, a pose estimation network is designed to estimate the poses of both the ego vehicle and the obstacles only with the continuous point clouds obtained by the LiDAR mounted on the ego vehicle. In the network, PointNet++ is utilized as the backbone to extract point-wise features and divide the points into the static part and the moving part. The former is used to estimate the ego vehicle's pose, while the latter is applied for the obstacle pose identification. Then, a reduced-order observer is designed to estimate the velocities, whose convergence is proved with the Lyapunov theory. Finally, both simulation and experiment results are provided to show the effectiveness of the proposed method.

We consider natural variants of Lehmer's unresolved conjecture that Ramanujan's tau-function never vanishes. Namely, for $n>1$ we prove that $$\tau(n)\not \in \{\pm 1, \pm 3, \pm 5, \pm 7, \pm 691\}.$$ This result is an example of general theorems for newforms with trivial mod 2 residual Galois representation, which will appear in forthcoming work of the authors with Wei-Lun Tsai. Ramanujan's well-known congruences for $\tau(n)$ allow for the simplified proof in these special cases. We make use of the theory of Lucas sequences, the Chabauty-Coleman method for hyperelliptic curves, and facts about certain Thue equations., Comment: To appear in JNT Prime. For more general results, see arXiv:2005.10354

A Direct Numerical Simulation (DNS) scheme for solving the temporal-spatial multiscale problem of sympathetic detonation is proposed. According to the physical and chemical properties, the SD process is divided into two stages in the numerical simulation. Two different grid sizes are used in the two stages to improve calculation accuracy and efficiency. The local Level Set Method is used to accurately track the multi-material interface, and the Harten Lax and van Leer Contact (HLLC) method is used to solve local Riemann problem. Based on the computing method mentioned above, choosing the high evolution Weighted Essentially Non-Oscillatory (WENO) scheme for the spatial discretization of governing equations in alliance with Total Variation Diminishing (TVD) Runge-Kutta for time discretization, the Eulerian code of SD is developed. It solves the data interaction between the two solvers with variable grid size and time step, and realizes the three-dimensional parallel computing of SD. Composition B is taken as the research object. The processes of SD in different stand-off distances are analyzed. The transmission and safety distances of Composition B are given. The test under the same conditions is designed to verify the numerical simulation. The numerical simulation results are in good agreement with the experiment, which proves the accuracy and feasibility of the numerical method.

This study obtains a fuzzy solution to the mathematical model of a cancer tumor under Caputo-generalized Hukuhara partial differentiability by using fuzzy integral transforms. In order to solve the fuzzy partial fractional differential equations, the two-variable fuzzy Laplace—Carson transforms and the two-variable fuzzy Fourier transform under the generalized Hukuhara partial differentiability and Caputo-generalized Hukuhara partial differentiability are investigated. Following this, an algorithm for the proposed method is presented. Finally, as a practical model of fuzzy fractional diffusion equations, the fuzzy mathematical model of the net killing rate of cancer cells in tumors is investigated. This technique is powerful and essential for the development of a fuzzy analytical method for solving fuzzy partial fractional differential equations. 2-s2.0-85119302470

The work is dedicated to a research into the physicochemical aspects of carbon nanopowder production from the gas phase by the pyrolysis of the initial precursors in a cyclic adiabatic process in a flow-through chemical compression reactor (CCR). The method allows the production of carbon nanomaterials, such as the carbon nanoparticles and globular soot particles, as well, as the core-shell structures representing the 4–7 nm composite particles with a SiC crystal core covered with graphene layers. Products have controlled and reproducible properties, and output capacity of the method is sufficient for technological purposes. The nanopowders are characterized by high-resolution transmission electron (TEM) and scanning electron (SEM) microscopy, X-ray diffraction analysis and Raman spectroscopy. The nanomaterials were used to manufacture their aqueous suspensions - nanofluids, which have shown their effectiveness in enhancing oil recovery in oil displacement technologies.

We address the motion planning problem for a robotic system whose configuration manifold contains a group of rotations. Our approach is applied to a free-floating space robot composed of a three-dimensional base (a spacecraft) and an anthropomorphic onboard manipulator. The robot is actuated by the torques exerted at the joints of the onboard manipulator. A coordinate-free representation of rotations is utilized. The Lagrangian formalism is employed in order to derive a dynamics model of the robot that takes the form of a control system defined on the group of rotations and the joint space of the onboard manipulator. Using the conservation of angular momentum of the robot, a Jacobian motion planning algorithm is designed relying on the Endogenous Configuration Space Approach. The performance of the algorithm is verified by computer simulations.

In this article, the problem of tracking control is considered for a class of uncertain strict-feedback nonlinear systems with deferred asymmetric time-varying full-state constraints. A novel adaptive robust full-state-constrained control scheme is developed. First, by introducing a novel shifting function, the original constrained system with any initial values is modified to a new constrained system, and the initial values of the modified constrained system remain 0. Then, to remove the feasibility condition caused by the barrier Lyapunov functions, the modified constrained system is further transformed into a new unconstrained system by a brand new nonlinear transformation. Furthermore, the tracking error system of the unconstrained system is constructed by using a new coordinate transformation, and a novel adaptive full-state-constrained control scheme is designed based on this error system through the backstepping recursion method and first-order filters. Finally, the resulting closed-loop system proves to be stable and numerical simulations are conducted to demonstrate the effectiveness of the developed control strategy.

In this article, we study the optimal control problem arising from the mean-field game formulation of the collective decision-making in honeybee swarms. A population of homogeneous players (the honeybees) has to reach consensus on one of two options. We consider three states: the first two represent the available options (or strategies), and the third one represents the uncommitted state. We formulate the continuous-time discrete-state mean-field game model. The contributions of this article are the following: 1) we propose an optimal control model where players have to control their transition rates to minimize a running cost and a terminal cost, in the presence of an adversarial disturbance; 2) we develop a formulation of the micro–macro model in the form of an initial-terminal value problem with switched dynamics; 3) we study the existence of stationary solutions and the mean-field Nash equilibrium for the resulting switched system; 4) we show that under certain assumptions on the parameters, the game may admit periodic solutions; and 5) we analyze the resulting microscopic dynamics in a structured environment where a finite number of players interact through a network topology.

Current interrater reliability (IRR) coefficients ignore the nested structure of multilevel observational data, resulting in biased estimates of both subject- and cluster-level IRR. We used generalizability theory to provide a conceptualization and estimation method for IRR of continuous multilevel observational data. We explain how generalizability theory decomposes the variance of multilevel observational data into subject-, cluster-, and rater-related components, which can be estimated using Markov chain Monte Carlo (MCMC) estimation. We explain how IRR coefficients for each level can be derived from these variance components, and how they can be estimated as intraclass correlation coefficients (ICC). We assessed the quality of MCMC point and interval estimates with a simulation study, and showed that small numbers of raters were the main source of bias and inefficiency of the ICCs. In a follow-up simulation, we showed that a planned missing data design can diminish most estimation difficulties in these conditions, yielding a useful approach to estimating multilevel interrater reliability for most social and behavioral research. We illustrated the method using data on student-teacher relationships. All software code and data used for this article is available on the Open Science Framework: https://osf.io/bwk5t/. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Introduction: Cannabidiol (CBD), cannabinol (CBN), and Δ9-tetrahydrocannabinol (Δ9-THC) are major cannabinoids in cannabis resin and products. The kinetic of the chemical reaction of resin cannabis is important for product development and storage. A few reports are available in the literature on the rate of CBD and Δ9-THC degradation, and CBN formation in dried resin and solutions of various pH. Materials and Methods: Thermal degradation of CBD, Δ9-THC, and formation of CBN was studied at 50°C, 60°C, 70°C, and 80°C for dried cannabis resin. The effect of pH and temperature on cannabinoids transformation in cannabis solution was also examined at pH 2, 4, 6, 8, 10, and 12 and at 40°C, 50°C, 60°C, and 70°C. High-performance chromatography coupled with diode-array detection (HPLC-DAD) was used for the analysis of CBD, CBN, and Δ9-THC transformation. The values of activation energies (Ea), shelf-life (t90% - t110%), and rate constant (k) were calculated for the CBD, Δ9-THC, and CBN. The effect of temperature and pH on the dried cannabis resin was adequately modeled with the Arrhenius equation. Results: The results indicated that the chemical kinetics in the thermal degradation of CBD, Δ9-THC, and formation of CBN were the zero-order, pseudo-zero-order, and first-order reactions, respectively, in cannabis resin. The first-order and pseudo-first-order degradation kinetics were evidenced for CBD and Δ9-THC, respectively, in cannabis solutions, whereas the zero-order formation kinetic was detected for the CBN. The transformation rate of the CBD, CBN, and Δ9-THC increased with increasing temperature, especially as temperature increased to 70°C at pH 2.0. The optimum pH for CBD stability was between pH 4 and 6, whereas the optimum pH for Δ9-THC stability was between pH 4 and 12. Conclusion: The major cannabinoids (CBD, CBN, and Δ9-THC) reacted more quickly at high temperature and in an acidic solution. Especially, the minimum transformation of CBD, CBN, and Δ9-THC was achieved by using on a low temperature, slightly to moderately acidic pH values, and short-time processing. These results may help to improve the storage condition of CBD, CBN, and Δ9-THC products and in the manufacturing process.

The multivariable modeling of a servo actuating system consisting of multi-field multi-armature direct current (MFMADC) machine is extracted and a novel discrete time nonlinear algorithm is proposed for the corresponding system. The proposed control algorithm demonstrates robustness against modeling uncertainty and by utilizing its novel mathematical structure, decouples the dynamical interactions of the connected motors. The main contribution of this paper is the proposition of a new decoupling control algorithm that in which, the driving (commanding) voltages of the connected driving motors are extracted separately and independently using the Lyapunov principle in discrete time. In fact, the obtained coupled stabilizing convex inequalities of the controlling voltages, resulting from the evaluation of the Lyapunov functions, are analytically decoupled using elementary matrix operations. Consequently, each motor now has the capability to perform its controlling task (position control or torque control) with asymptotic stability and robustness against uncertainty. To assess the performance of the proposed controlling algorithm and its verification, a MFMADC machine is attached to a harmonic drive reducer (HDR) whose flex spline and circular spline are fabricated using viscoelastic polyesters PLA and thermoplastic PLA, respectively. A number of experiments are conducted where in the first test, the MFMADC is controlled in only-position mode while in the second test, the MFMADC is controlled in simultaneous position-torque control mode. Comparative assessments confirm that the MFMADC technology is needed when a high precision tracking of position, under high frequency disturbances, is desired.

Author(s): Siracusa, M; Delsozzo, E; Rabozzi, M; Di Tucci, L; Williams, S; Sciuto, D; Santambrogio, MD | Abstract: With reconfigurable fabrics delivering increasing performance over the years, Field-Programmable Gate Arrays (FPGAs) are becoming an appealing solution for next-generation High-Performance Computing (HPC) systems. However, in order to gain traction among traditional Von Neumann architectures, the optimization process of FPGA designs should be further abstracted to a higher level. In fact, while High-Level Synthesis (HLS) already provides a handy way to write FPGA code with procedural languages, substantial effort and expertise are still required to optimize the resulting FPGA design for the underlying hardware. To overcome this problem, we propose a semi-automated performance optimization methodology based on a Hierarchical Roofline model for FPGAs. System-wide and applications-specific optimizations such as off-chip memory transfer and data locality optimizations are guided by the FPGA Roofline model whereas FPGA-specific optimizations are automatically searched by a Design Space Exploration (DSE) engine. We demonstrate how this methodology allows to easily analyze and optimize a wide set of applications ranging from particle methods, wavefront algorithms, and sparse arithmetic computations. In addition, we illustrate how the integrated DSE engine achieves a 14.36x maximum speedup if compared to previous automated solutions in the literature.

Due to the nature of high mobility and dynamic network topology, Intrusion Detection Systems (IDSs) in Vehicular Ad-hoc Networks (VANETs) face lots of challenges, especially in balancing the accuracy and efficiency of detection. Current researches about the deployment of IDSs in VANETs mainly focus on a tradeoff between the effectiveness and efficiency, but few efforts have been done about the adaptability of the tradeoff in the changeable networks. Thus, we address two crucial problems: 1) how to perceive the environmental change in the perspective of an IDS? 2) how to make the IDS adaptive in different scenarios? In this paper, a Bayesian Game theory and Deep Q-learning Network-based IDS is proposed for VANETs, called GaDQN-IDS. The interactions between an IDS and attackers are formulated as a dynamic intrusion detection game, in which the IDS decides either to just adjust the tradeoff between the accuracy and efficiency or to be retrained completely when its detection capacity has declined. The Nash Equilibria (NE) of the game is derived to reveal how the optimal decision of the IDS depends on the detection performance and road conditions. Moreover, a Deep Q-learning Network (DQN)-Adjustment is proposed to realize the self-adaptation of the IDS in the dynamic game, while an Error Priority Learning (EPL) is further designed for IDS retraining in changing VANETs. Simulation results show that the GaDQN-IDS has better performance than other existing IDSs with higher detection rate as well as lower detection time and overhead.

This paper presents a hierarchical Green-Light Approach Speed (h-GLAS) strategy for controlling heavy-duty trucks traveling through urban/suburban environments, where future intersection signal phase and timing (SPaT) information is non-deterministic. Through vehicle-to-infrastructure communication, past SPaT information is sent to the vehicle and is used to forecast future predictions of the signal timing using a Gaussian Process (GP) model. The h-GLAS strategy uses the predicted SPaT information to generate an efficient desired velocity profile that navigates through intersections where the probability of a green light is maximized. This velocity profile is tracked by a convex model predictive controller (MPC) that simultaneously minimizes mechanical energy expenditure and braking effort over its prediction horizon. Downstream from the MPC, we implement a command governor (CG) that adjusts the MPC output by the minimum amount necessary to maintain safe vehicle following and ensure that the vehicle stops at all red lights. Using a proprietary medium-fidelity Simulink model provided by Volvo Trucks, we characterize the h-GLAS strategy's performance over a real suburban route, consisting of 10 actuated signalized intersections, using actual SPaT information provided by the NC Department of Traffic (DOT). Simulation results demonstrate a 30-43% reduction in fuel consumption, as compared to a baseline control strategy, which is attributable primarily to avoiding the massive energy losses associated with braking. Furthermore, we evaluate the computational efficiency of our approach by assessing average simulation execution times.

This paper details a method to improve accuracy of the land vehicle attitude estimation. It employs the vehicle model constraints to enhance the integration of GPS and a low-cost MEMS inertial measurement unit (MIMU) (GPS/MIMU). To improve the yaw angle estimation, we propose a lateral velocity constraint (LVC) aided method based on the observability analysis for the integrated fusion system. The theoretical analysis indicates that the yaw angle will be directly observed if LVC is augmented into the GPS/MIMU fusion algorithm. Furthermore, for LVC/MIMU system without GPS, the roll angle is always well estimated regardless of whether the vehicle is moving or stationary, and, surprisingly, the pitch angle can also be relatively accurately estimated if the vehicle is moving. Thus, when GPS is unavailable, the LVC/MIMU fusion is proposed by replacing the GPS measurement with the virtual measurement LVC. It is an encouraging improvement since the accumulating errors induced by gyro bias can be suppressed using MIMU alone. Moreover, to overcome the shortage that the pitch angle is not observed if the vehicle is stationary, we propose to fuse the gyros with gravity-based angles by Kalman filtering if the vehicle is detected motionless based on a switching rule. The experimental results compare favorably to theoretical analysis, showing that the proposed method can effectively improve the accuracy for land vehicle attitude estimation.

This article proposes an adaptive neural network (NN) control method for an n -link constrained robotic manipulator. Driven by actual demands, manipulator and actuator dynamics, state and input constraints, and unknown time-varying delays are taken into account simultaneously. NNs are employed to approximate unknown nonlinearities. Time-varying barrier Lyapunov functions are utilized to cope with full-state constraints. By resorting to saturation function and Lyapunov-Krasovskii functionals, the effects of actuator saturation and time delays are eliminated. It is proved that all the closed-loop signals are semiglobally uniformly ultimately bounded, full-state constraints and actuator saturation are not violated, and error signals remain within compact sets around zero. Simulation studies are given to demonstrate the validity and advantages of this control scheme.

In the study, the layered double hydroxide (LDH) of NiZnFe and its composites with date-palm biochar (LDH-DPb) and carbon nanotubes (LDH-cnt) were synthesized for adsorbing reactive black 5 (RB5) dye from aqueous solutions. In the first 5 min, rapid adsorption was followed by a gradual increase in both dye uptake and removal efficiency of up to 60 min of starting time. In the investigated pH range (3.0–8.0), the removal efficiency linearly decreased while the sorption capacity linearly increased for all three adsorbents as their doses increased to 0.3 or 0.4 g following a decreasing trend up to 0.6 g. By increasing the initial RB5 concentration from 10 to 100 mg L−1, the removal efficiency linearly decreased. A nearly perfect fitting of the pseudo-second-order kinetic model to the adsorption data was observed; however, the Elovich kinetic model showed the heterogeneous surface of adsorbents with chemisorption. At the solid–liquid interface, from a thermodynamics point of view, we obtained the nonspontaneous nature of the adsorption of RB5 dye of the studied adsorbents with an increased disorder, which supported the endothermic nature onto the studied adsorption process. Furthermore, a nearly perfect fitting of the Langmuir model was obtained to the adsorption data, thereby suggesting the monolayer adsorption of RB5 dye onto the studied adsorbents. In the Dubinin–Radushkevich model, a good agreement of the calculated adsorption capacities to the experimental values were observed and the chemical adsorption of RB5 dye on to the studied adsorbents was proposed based on E (8 – 16 kJ mol−1).

Many engineering products have more than one failure mode and the evolution of each mode can be monitored by measuring a performance characteristic (PC). It is found that the underlying multi-dimensional degradation often occurs with inherent process stochasticity and heterogeneity across units, as well as dependency among PCs. To accommodate these features, in this paper, we propose a novel multivariate degradation model based on the inverse Gaussian process. The model incorporates random effects that are subject to a multivariate normal distribution to capture both the unit-wise variability and the PC-wise dependence. Built upon this structure, we obtain some mathematically tractable properties such as the joint and conditional distribution functions, which subsequently facilitate the future degradation prediction and lifetime estimation. An expectation-maximization algorithm is developed to infer the model parameters along with the validation tools for model checking. In addition, two simulation studies are performed to assess the performance of the inference method and to evaluate the effect of model misspecification. Finally, the application of the proposed methodology is demonstrated by two illustrative examples.

In this paper, we study the phenomenon of concentration and the formation of delta shock wave in vanishing adiabatic exponent limit of Riemann solutions to the Aw–Rascle traffic model. It is proved that as the adiabatic exponent vanishes, the limit of solutions tends to a special delta-shock rather than the classical one to the zero pressure gas dynamics. In order to further study this problem, we consider a perturbed Aw–Rascle model and proceed to investigate the limits of solutions. We rigorously proved that, as the adiabatic exponent tends to one, any Riemann solution containing two shock waves tends to a delta-shock to the zero pressure gas dynamics in the distribution sense. Moreover, some representative numerical simulations are exhibited to confirm the theoretical analysis.

In this paper, periodic event-triggered formation control problems with collision avoidance are studied for leader–follower multiple Unmanned Aerial Vehicles (UAVs). Firstly, based on the Artificial Potential Field (APF) method, a novel sliding manifold is proposed for controller design, which can solve the problem of collision avoidance. Then, the event-triggered strategy is applied to the distributed formation control of multi-UAV systems, where the evaluation of the event condition is continuous. In addition, the exclusion of Zeno behavior can be guaranteed by the inter-event time between two successive trigger events have a positive lower bound. Next, a periodic event-triggered mechanism is developed for formation control based on the continuous event-triggered mechanism. The periodic trigger mechanism does not need additional hardware circuits and sophisticated sensors, which can reduce the control cost. The stability of the control system is proved by the Lyapunov function method. Finally, some numerical simulations are presented to illustrate the effectiveness of the proposed control protocol.

This article studies the leader-following consensus of multiagent systems (MASs) with completely event-triggered mechanisms (CETMs) and directed switching topologies. When most event-triggered schemes in MASs only reduce each agent's data transmissions to neighbor agents, CETMs further reduce data transmissions to actuators with one triggered function in each agent. To guarantee the switching links utilized by CETMs contain a directed spanning tree at any time, triggered decisions are improved to include the instants at which each agent's output links or leader link change. Furthermore, a less conservative method based on matrix inequalities is combined to minimize the allowable average dwell time (ADT) of switching topologies under the design conditions of CETMs. Based on multiple Lyapunov functions (MLFs), the sufficient conditions for controller gains that guarantee the leader-following consensus of MASs are proposed when the ADT of switching topologies is larger than the allowable value. Finally, an example of autonomous underwater vehicles (AUVs) is given to illustrate the effectiveness of CETMs.

This work presents the synthesis of Y2O3:Eu3+,xCa2+ (x = 0 mol%, 1 mol%, 3 mol%, 5 mol%, 7 mol%, 9 mol%, 11 mol% of Ca2+) nanophosphor with enhanced photoluminescence properties through a facile solution combustion method for optoelectronic, display, and lighting applications. The X-ray diffraction patterns (XRD) of the proposed nanophosphor reveal its structural properties and crystalline nature. The transmission electron microscope results (TEM) confirm the change in the shape of the particle and aggregation of particles after co-doping with Ca2+. Fourier transform infra-red spectroscopy (FTIR) and Raman vibrations also confirm the presence of Y-O vibration and subsequently explain the crystalline nature, structural properties, and purity of the samples. All the synthesized nanophosphors samples emit intense red emission at 613 nm (5D0→7F2) under excitation with 235, 394 and 466 nm wavelengths of Eu3+ ions. The photoluminescence (PL) emission spectra excited with 235 nm illustrates the highest emission peak with two other emission peaks excited with 466 and 394 nm that is 1.4 times higher than 466 nm and 1.9 times enhance by 394 nm wavelength, respectively. The emission intensity of Y2O3:Eu3+,xCa2+ (5 mol%) has been increased 8-fold as compared to Eu:Y2O3. Doping with Ca2+ ions enhances the emission intensity of Eu:Y2O3 nanophosphors due to an increase in energy transfer in Ca2+→Eu3+ through asymmetry in the crystal field and by introduction of radiative defect centers through oxygen vacancies in the yttria matrix. It has also been observed the optical band gap and the lifetime of the 5D0 level of Eu3+ ions in Y2O3:Eu3+,xCa2+ nanophosphor sample gets changed with a doping concentration of Ca2+ ions. Nanophosphor also reveals high thermal stability and quantum yield as estimating activation energy 0.25 eV and 81%, respectively. CIE, CCT, and color purity values (>98%) show an improved red-emitting nanophosphor in the warm region of light, which makes this material superior with a specific potential application for UV-based white LEDs with security ink, display devices, and various other optoelectronics devices.

We consider a Bessel process X of dimension δ ∈ ( 0 , 2 ) having 0 as a slowly reflecting (sticky) boundary point with a stickiness parameter 1 ∕ μ ∈ ( 0 , ∞ ) . We show that (i) the process X can be characterised through its square Y = X 2 as a unique weak solution to the SDE system d Y t = δ I ( Y t > 0 ) d t + 2 Y t d B t I ( Y t = 0 ) d t = 1 2 μ d l t 0 ( Y ) where B is a standard Brownian motion and l 0 ( Y ) is a diffusion local time process of Y at 0, and (ii) the transition density function of X can be expressed in the closed form as a convolution integral involving a Mittag-Leffler function and a modified Bessel function of the second kind. Appearance of the Mittag-Leffler function is novel in this context. We determine a (sticky) boundary condition at zero that characterises the transition density function of X as a unique solution to the Kolmogorov backward/forward equation of X . We also show that the convolution integral can be characterised as a unique solution to the generalised Abel equation of the second kind. Letting μ ↓ 0 (absorption) and μ ↑ ∞ (instantaneous reflection) the closed-form expression for the transition density function of X reduces to the ones found by Feller (1951) and Molchanov (1967) respectively.

This study aims to determine the galloping trend of iced transmission lines. A mathematical model considering the first three torsional modes is first established by applying the Galerkin method to the continuous dynamic model. The Hopf bifurcation theory is applied to the linear part of the mathematical model to calculate the critical stability conditions for each mode. Numerical procedures are then implemented on the mathematical model to simulate the galloping feature. An experiment on a continuous model for conductor galloping is designed to validate the above-mentioned galloping feature. The phenomena observed in the wind tunnel test are in good agreement with the theoretical observations. The results show that torsional galloping modifies the numbers of bifurcation points for the in-plane modes. A lower in-plane mode is gradually substituted by a higher one with increasing wind velocity. The theoretical and experimental results are then compared in terms of the galloping frequency and wind velocity range of galloping. By applying the experimental parameters to the theoretical model, the consistency between the theory and the experiment is proven. A convenient method is proposed to predict the wind speed range of galloping and is expected to be applicable for predictions of other critical parameters.

Due to the emergence of mobile 3D devices, multi-view 3D videos are expected to play increasingly important roles in providing immersively interactive experiences to users. Compared with traditional single-view videos, it is envisaged that a multi-view 3D video requires larger storage space and bandwidth consumption. Nevertheless, efficient caching for multi-view 3D videos in a mobile proxy has not been explored in the literature. In this paper, therefore, we first observe that the storage space can be effectively reduced by leveraging Depth Image Based Rendering (DIBR) in multi-view 3D videos. We then formulate a new cache management problem, named Adaptive View Selection and Cache Operation (AVSCO), and find the optimal policy based on Markov Decision Process (MDP). Afterward, we propose an approximate MDP to effectively aggregate similar states to reduce the MDP state space and devise an efficient and effective algorithm to support human visual continuity in AVSCO. Simulation results manifest that the proposed algorithms can significantly improve the cache hit rate and reduce the bandwidth consumption for the remote access compared with the existing cache replacement algorithms.

This article investigates the optimally distributed consensus control problem for discrete-time multiagent systems with completely unknown dynamics and computational ability differences. The problem can be viewed as solving nonzero-sum games with distributed reinforcement learning (RL), and each agent is a player in these games. First, to guarantee the real-time performance of learning algorithms, a data-based distributed control algorithm is proposed for multiagent systems using offline system interaction data sets. By utilizing the interactive data produced during the run of a real-time system, the proposed algorithm improves system performance based on distributed policy gradient RL. The convergence and stability are guaranteed based on functional analysis and the Lyapunov method. Second, to address asynchronous learning caused by computational ability differences in multiagent systems, the proposed algorithm is extended to an asynchronous version in which executing policy improvement or not of each agent is independent of its neighbors. Furthermore, an actor-critic structure, which contains two neural networks, is developed to implement the proposed algorithm in synchronous and asynchronous cases. Based on the method of weighted residuals, the convergence and optimality of the neural networks are guaranteed by proving the approximation errors converge to zero. Finally, simulations are conducted to show the effectiveness of the proposed algorithm.

Objective The aim of this study is to develop a new method that will facilitate the diagnosis of sleep breathing disorders (SBD; primary snoring and obstructive sleep apnea syndrome (OSAS)). Methods In this study, we had 22 volunteers, 16 men and 6 women, having the complaint of snoring (Age; 49.2 ± 6.76 and Body Mass Index 42.6 ± 8.16); their diagnoses of OSAS and primary snoring were established with polysomnography (PSG). The number and severity of apnea episodes were compared between the classical method PSG and the new method SNOROSALAB. Results As a result, no statistically significant differences could be found between the two methods (Paired Sample Test: p=0.78; Pearson Correlation Coefficient: 0.999 ⁎ ⁎ ⁎ ). After “Kolmogorov-Smirnov” analysis, “Mann-Whitney U” test was performed and we demonstrated that there was not any statistical difference between the two methods (Mann-Whitney U value: 224.5; p>0.05). When comparisons were made with Kohen's kappa criteria, the numbers of apnea identified by the two methods did not differ significantly (kappa value: −0.002; p>0.05). Conclusions SNOROSALAB is a simple voice-recording device that does not have any cables attached and it is easy to use. It serves as a screening and diagnostic tool that attempts to detect individuals with suspected snoring and sleep apnea before they enter PSG at their homes.

Human-machine shared control has become an effective cutting-edge approach to enhance driving safety and assist in the transition from manual to autonomous driving. However, driving authority allocation when conflict occurs between a human driver and a machine still represents an intractable problem. In order to solve this problem, this paper proposes an adaptive steering torque coupling framework to achieve the human-machine shared control with conflict resolution. Additionally, a driver trajectory prediction method considering human-machine interaction is established based on the extended state observer and long short-term memory network. In addition, the human-machine shared steering system is modeled based on a non-cooperative dynamic game by the prediction model method for path-tracking, and the obtained solution is given according to the Nash equilibrium. Particularly, a dynamic load allocation approach is designed to resolve human-machine conflict and relax the driver. In order to verify the strategy formed based on the proposed framework, a driver-in-the-loop experiment is conducted. The experimental results reveal that the proposed strategy can effectively reduce the human-machine conflict torque and ensure a driver has absolute control authority. Furthermore, the proposed strategy can also transfer the driving workload between the driver and machine, provide driving experience, or improve driving comfort when there is no conflict.

A new passivity-based controller is proposed for stable co-transportation of a cable-suspended payload by a number of quad-rotors. Nominal proportional-derivative position controllers with desired acceleration feedforward are augmented with time-varying dissipative terms to account for the drones under-actuation. A storage function is defined that includes terms inspired by the kinetic and potential energies of the system components as well as virtual energy of the controller. A time-domain passivity observer is used in conjunction with an adaptive dissipative term to ensure that the value of the storage function is continuously decreased and hence, guarantees closed-loop stability. Moreover, barrier Lyapunov functions are employed to avoid inter-drone collisions. The proposed controller makes no assumption about and requires no knowledge of the cables tension status. Additionally, it requires no measurement from the payload. The cables can be attached to the quad-rotors and the payload at arbitrary points. Experiments with a system of three quad-rotors and a cable-suspended payload demonstrate the effectiveness of the proposed approach.